This file is used to prepare the figures for the paper.

library(dplyr)
library(patchwork)
library(ggplot2)
library(ComplexHeatmap)
library(org.Mm.eg.db)

.libPaths()
## [1] "/usr/local/lib/R/library"

Preparation

Here are the folders where analyzes are stored :

data_dir = "./.."
list.files(data_dir)
## [1] "0_intro"      "1_metadata"   "2_individual" "3_combined"   "4_zoom"      
## [6] "5_figures"    "LICENSE"      "index.html"   "index_layout"

We load the dataset containing all cells :

sobj = readRDS(paste0(data_dir, "/3_combined/hs_hd_sobj.rds"))
sobj
## An object of class Seurat 
## 20003 features across 12111 samples within 1 assay 
## Active assay: RNA (20003 features, 2000 variable features)
##  6 dimensional reductions calculated: RNA_pca, RNA_pca_38_tsne, RNA_pca_38_umap, harmony, harmony_38_umap, harmony_38_tsne

These are all the samples analyzed :

sample_info = readRDS(paste0(data_dir, "/1_metadata/hs_hd_sample_info.rds"))

# Nb cells by dataset
to_plot = table(sobj$sample_identifier) %>%
  as.data.frame.table(., stringsAsFactors = FALSE) %>%
  `colnames<-`(c("sample_identifier", "nb_cells")) %>%
  dplyr::left_join(x = ., y = sample_info, by = "sample_identifier") 

# patchwork
plot_list = aquarius::fig_plot_gb(to_plot, title = "Available datasets")
patchwork::wrap_plots(plot_list) +
  patchwork::plot_layout(design = "A\nB", heights = c(0.1,5)) &
  ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold", size = 15))

These are the custom colors for cell populations :

color_markers = readRDS(paste0(data_dir, "/1_metadata/hs_hd_color_markers.rds"))
color_markers = color_markers[names(color_markers) != "melanocytes"]
ors_color = color_markers["ORS"]
color_markers["ORS"] = color_markers["IFE"] 
color_markers["IFE"] = ors_color
color_markers["B cells"] = "chocolate3"
rm(ors_color)

data.frame(cell_type = names(color_markers),
           color = unlist(color_markers)) %>%
  ggplot2::ggplot(., aes(x = cell_type, y = 0, fill = cell_type)) +
  ggplot2::geom_point(pch = 21, size = 5) +
  ggplot2::scale_fill_manual(values = unlist(color_markers), breaks = names(color_markers)) +
  ggplot2::theme_classic() +
  ggplot2::theme(legend.position = "none",
                 axis.line = element_blank(),
                 axis.title = element_blank(),
                 axis.ticks = element_blank(),
                 axis.text.y = element_blank(),
                 axis.text.x = element_text(hjust = 1, angle = 20))

We define custom colors for sample type :

sample_type_colors = setNames(nm = levels(sample_info$sample_type),
                              c("#C55F40", "#2C78E6"))

data.frame(cell_type = names(sample_type_colors),
           color = unlist(sample_type_colors)) %>%
  ggplot2::ggplot(., aes(x = cell_type, y = 0, fill = cell_type)) +
  ggplot2::geom_point(pch = 21, size = 5) +
  ggplot2::scale_fill_manual(values = unlist(sample_type_colors), breaks = names(sample_type_colors)) +
  ggplot2::theme_classic() +
  ggplot2::theme(legend.position = "none",
                 axis.line = element_blank(),
                 axis.title = element_blank(),
                 axis.ticks = element_blank(),
                 axis.text.y = element_blank())

We set a background color :

bg_color = "gray94"

This is the correspondence between cell types and cell families, and custom colors to color cells by cell family :

custom_order_cell_type = data.frame(
  cell_type = names(color_markers),
  cell_family = c(rep("immune cells", 5),
                  rep("matrix", 5),
                  rep("non matrix", 5)),
  stringsAsFactors = FALSE)
custom_order_cell_type$cell_type = factor(custom_order_cell_type$cell_type,
                                          levels = custom_order_cell_type$cell_type)
rownames(custom_order_cell_type) = custom_order_cell_type$cell_type

family_color = c("immune cells" = "slateblue1",
                 "matrix" = "mediumseagreen",
                 "non matrix" = "firebrick3")

We load markers to display on a dotplot to assess cell type annotation :

dotplot_markers = readRDS(paste0(data_dir, "/1_metadata/hs_hd_dotplot_markers.rds"))
dotplot_markers = dotplot_markers[names(dotplot_markers) != "melanocytes"]
lengths(dotplot_markers)
##      CD4 T cells      CD8 T cells Langerhans cells      macrophages 
##                2                2                2                2 
##          B cells          cuticle           cortex          medulla 
##                2                2                2                2 
##              IRS    proliferative              IBL              ORS 
##                2                2                2                2 
##              IFE             HFSC        sebocytes 
##                2                2                2

Custom functions to display gene expression on the heatmap :

color_fun = function(one_gene) {
  gene_range = range(ht_annot[, one_gene])
  gene_palette = circlize::colorRamp2(colors = c("#FFFFFF", aquarius::color_gene[-1]),
                                      breaks = seq(from = gene_range[1], to = gene_range[2],
                                                   length.out = length(aquarius::color_gene)))
  return(gene_palette)
}

All samples

Settings

This is the projection name to visualize cells :

name2D = "harmony_38_tsne"
name2D_atlas = name2D

Preparation

We make a low resolutive clustering for the heatmap :

sobj = Seurat::FindClusters(sobj, resolution = 0.4)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 12111
## Number of edges: 475472
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9421
## Number of communities: 17
## Elapsed time: 1 seconds
length(levels(sobj$seurat_clusters))
## [1] 17

We define cluster type and cluster family :

cluster_type = table(sobj$cell_type, sobj$seurat_clusters) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
                        levels(sobj$cell_type)[cluster_type])

sobj$cluster_type = cluster_type[sobj$seurat_clusters]
sobj$cluster_type = factor(sobj$cluster_type,
                           levels = levels(sobj$cell_type))
sobj$cluster_family = custom_order_cell_type[sobj$cluster_type, "cell_family"]
sobj$cluster_family = factor(sobj$cluster_family,
                             levels = names(family_color))

Figures

Project name :

# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj), replace = FALSE, size = ncol(sobj))

# Extract coordinates
cells_coord = sobj@reductions[[name2D]]@cell.embeddings %>%
  as.data.frame() %>%
  `colnames<-`(c("Dim1", "Dim2"))
cells_coord$project_name = sobj$project_name
cells_coord = cells_coord[(rnd_order), ]

# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = project_name)) +
  ggplot2::geom_point(size = 0.5) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  ggplot2::theme_void() +
  ggplot2::theme(aspect.ratio = 1,
                 legend.position = "none")

Cluster :

Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.4,
                group.by = "seurat_clusters", label = TRUE) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()

Cell type annotation :

Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
                group.by = "cell_type", cols = color_markers) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()

Cluster family annotation :

Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
                group.by = "cluster_family", cols = family_color) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()

Cell type annotation split by condition :

plot_list = aquarius::plot_split_dimred(sobj, reduction = name2D,
                                        group_by = "cell_type",
                                        group_color = color_markers,
                                        split_by = "sample_type",
                                        split_color = sample_type_colors,
                                        bg_color = bg_color)

patchwork::wrap_plots(plot_list) &
  Seurat::NoLegend()

Gene expression to assess annotation :

genes = c("PTPRC", "MSX2", "KRT14")
names(genes) = c("immune cells", "matrix cells", "non-matrix cells")

plot_list = lapply(c(1:length(genes)), FUN = function(gene_id) {
  gene = genes[[gene_id]]
  pop = names(genes)[gene_id]
  
  sobj$my_gene = Seurat::FetchData(sobj, gene)[, 1] %>%
    aquarius::run_rescale(., new_min = 0, new_max = 10)
  
  Seurat::FeaturePlot(sobj, features = "my_gene", reduction = name2D) +
    ggplot2::scale_color_gradientn(colors = aquarius::color_gene,
                                   breaks = seq(0, 10, by = 2.5),
                                   labels = c("min", rep("", 3), "max")) +
    ggplot2::labs(title = gene) + 
    # subtitle = pop) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5, size = 17),
                   plot.subtitle = element_text(hjust = 0.5, size = 15),
                   legend.text = element_text(size = 15),
                   legend.position = "none") +
    Seurat::NoAxes()
})

plot_list
## [[1]]

## 
## [[2]]

## 
## [[3]]

Barplot by cluster family :

quantif = table(sobj$sample_identifier) %>%
  as.data.frame.table() %>%
  `colnames<-`(c("Sample", "nb_cells"))

aquarius::plot_barplot(df = table(sobj$sample_identifier,
                                  sobj$cluster_family) %>%
                         as.data.frame.table() %>%
                         `colnames<-`(c("Sample", "Cell Type", "Number")),
                       x = "Sample", y = "Number", fill = "Cell Type",
                       position = position_fill()) +
  ggplot2::geom_label(data = quantif, inherit.aes = FALSE,
                      aes(x = .data$Sample, y = 1.05, label = .data$nb_cells),
                      label.size = 0, size = 5) +
  ggplot2::scale_fill_manual(values = unlist(family_color),
                             breaks = names(family_color),
                             name = "Cell Family") +
  ggplot2::scale_y_continuous(breaks = seq(0, 1, by = 0.25),
                              labels = paste0(seq(0, 100, by = 25), sep = " %"),
                              expand = ggplot2::expansion(add = c(0, 0.05))) +
  ggplot2::theme(axis.title.y = element_blank(),
                 axis.line.x = element_line(colour = "lightgray"),
                 text = element_text(size = 15),
                 legend.position = "none")

Heatmap of cluster proportion by sample :

group_by = "seurat_clusters"

cluster_by_sample = table(sobj$sample_identifier,
                          sobj@meta.data[, group_by]) %>%
  prop.table(margin = 1) %>%
  as.matrix()

## Representative markers
cluster_markers = c("PTPRC", "CD3E", "AIF1", "MSX2", "DIO2", "GPX2", "KRT16")
ht_annot = Seurat::GetAssayData(sobj, assay = "RNA", slot = "data")[cluster_markers, ] %>%
  Matrix::t() %>% as.data.frame()
ht_annot$clusters = sobj@meta.data[, group_by]
ht_annot = ht_annot %>%
  dplyr::group_by(clusters) %>%
  dplyr::summarise_all(funs('mean' = mean)) %>%
  as.data.frame() %>%
  dplyr::select(-clusters) %>%
  `colnames<-`(c(cluster_markers))

## Bottom annotation : average gene expression
ha_bottom = ComplexHeatmap::HeatmapAnnotation(df = ht_annot,
                                              which = "column",
                                              show_legend = FALSE,
                                              col = setNames(nm = cluster_markers,
                                                             lapply(cluster_markers, FUN = color_fun)),
                                              annotation_name_side = "right")

## Right annotation : number of cells by dataset
ht_annot = table(sobj$sample_identifier) %>%
  as.data.frame.table() %>%
  `colnames<-`(c("sample_identifier", "nb_cells")) %>%
  `rownames<-`(.$sample_identifier) %>%
  dplyr::select(-sample_identifier)

ha_right = ComplexHeatmap::HeatmapAnnotation(
  df = ht_annot,
  which = "row",
  show_legend = TRUE,
  annotation_name_side = "top",
  col = list(nb_cells  = circlize::colorRamp2(colors = RColorBrewer::brewer.pal(name = "Greys", n = 9),
                                              breaks = seq(from = range(ht_annot$nb_cells)[1],
                                                           to = range(ht_annot$nb_cells)[2],
                                                           length.out = 9))))

## Left annotation : gender
ha_left = ComplexHeatmap::HeatmapAnnotation(
  gender = sample_info$gender,
  which = "row",
  show_legend = TRUE,
  annotation_name_side = "bottom",
  col = list(gender = setNames(nm = c("F", "M"),
                               c("lightcyan3", "navyblue"))))

## Top annotation : main cell type in this cluster
ht_annot = table(sobj$sample_identifier,
                 sobj@meta.data[, group_by]) %>%
  prop.table(margin = 1) %>%
  as.matrix()

ht_annot = table(sobj$cell_type,
                 sobj@meta.data[, group_by]) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
ht_annot = data.frame(row.names = names(ht_annot),
                      cell_type = names(color_markers)[ht_annot],
                      stringsAsFactors = FALSE)
ht_annot = dplyr::left_join(ht_annot, custom_order_cell_type, by = "cell_type") %>%
  # Simplification for matrix
  dplyr::mutate(cell_type = ifelse(cell_type %in% c("medulla", "cortex", "cuticle"), yes = "hair shaft", no = cell_type)) %>%
  # Simplification for T cells
  dplyr::mutate(cell_type = ifelse(cell_type %in% c("CD4 T cells", "CD8 T cells"), yes = "T cells", no = cell_type)) %>%
  # Simplification for APC
  dplyr::mutate(cell_type = ifelse(cell_type %in% c("Langerhans cells", "macrophages"), yes = "APC", no = cell_type)) %>%
  # Add color
  dplyr::mutate(color = as.character(color_markers[cell_type])) %>%
  dplyr::mutate(color = ifelse(cell_type == "hair shaft", yes = "#FFB6C1", no = color)) %>%
  dplyr::mutate(color = ifelse(cell_type == "T cells", yes = "#8A6EE6", no = color)) %>%
  dplyr::mutate(color = ifelse(cell_type == "APC", yes = "#9CAA4B", no = color))

ha_top = ComplexHeatmap::HeatmapAnnotation(
  cell_type = ht_annot$cell_type,
  # cell_family = ht_annot$cell_family,
  which = "column",
  show_legend = TRUE,
  annotation_name_side = "left",
  col = list(cell_type = setNames(nm = ht_annot$cell_type,
                                  ht_annot$color)
             #, cell_family = family_color
  ))

## Assemble heatmap
ht = ComplexHeatmap::Heatmap(cluster_by_sample,
                             heatmap_legend_param = list(title = "Proportion",
                                                         col = c("#2166AC", "#F7F7F7", "#B2182B")),
                             # bottom_annotation = ha_bottom,
                             right_annotation = ha_right,
                             left_annotation = ha_left,
                             top_annotation = ha_top,
                             cluster_rows = TRUE,
                             cluster_columns = TRUE,
                             row_title = "Sample",
                             row_names_gp = grid::gpar(names = sample_info$sample_identifier,
                                                       col = sample_info$color,
                                                       fontface = "bold"),
                             column_title = "Cluster",
                             column_names_centered = TRUE,
                             row_names_side = "left",
                             column_names_side = "top",
                             column_names_rot = 0)

## Draw !
ComplexHeatmap::draw(ht, merge_legends = TRUE)

Dotplot :

plot_list = aquarius::plot_dotplot(sobj,
                                   markers = c("PTPRC",
                                               "CD3E", "CD4",
                                               "CD3E", "CD8A",
                                               "CD207", "CPVL", 
                                               "TREM2", "MSR1",
                                               "CD79A", "CD79B",
                                               "KRT32", "KRT35",
                                               "KRT31", "PRR9",
                                               "BAMBI", "ADLH1A3",
                                               "KRT71", "KRT73",
                                               "TOP2A", "MCM5",
                                               "KRT16", "KRT6C",
                                               "KRT15", "GPX2",
                                               "SPINK5", "LY6D",
                                               "DIO2", "TCEAL2",
                                               "CLMP", "PPARG"),
                                   assay = "RNA", column_name = "cell_type", nb_hline = 0) +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  ggplot2::theme(legend.position = "right",
                 legend.box = "vertical",
                 axis.title = element_blank(),
                 axis.ticks.x = element_blank(),
                 axis.text.x = element_blank(),
                 axis.line.x = element_blank(),
                 plot.margin = unit(rep(0, 4), "cm"))

p = ggplot2::ggplot(custom_order_cell_type, aes(x = cell_type, y = 0)) +
  ggplot2::geom_point(size = 0) +
  ggplot2::geom_segment(aes(x = 0.5, xend = 5.5, y = 0, yend = 0), size = 6, col = family_color["immune cells"]) +
  ggplot2::geom_segment(aes(x = 5.5, xend = 10.5, y = 0, yend = 0), size = 6, col = family_color["matrix"]) +
  ggplot2::geom_segment(aes(x = 10.5, xend = 15.5, y = 0, yend = 0), size = 6, col = family_color["non matrix"]) +
  ggplot2::scale_y_continuous(expand = c(0,0), limits = c(0,0)) +
  ggplot2::theme_classic() +
  ggplot2::theme(axis.text.y = element_blank(),
                 axis.ticks.y = element_blank(),
                 axis.title = element_blank(),
                 axis.line.y = element_blank(),
                 axis.text.x = element_text(angle = 45, hjust = 1, size = 10, color = "black"),
                 plot.margin = unit(rep(0, 4), "cm"))

plot_list = patchwork::wrap_plots(plot_list, p,
                                  ncol = 1, heights = c(25, 1))
plot_list

Immune cells

Settings

We load the immune cells dataset :

sobj_ic = readRDS(paste0(data_dir, "/4_zoom/1_zoom_immune/immune_cells_sobj.rds"))
sobj_ic
## An object of class Seurat 
## 15121 features across 2329 samples within 1 assay 
## Active assay: RNA (15121 features, 2000 variable features)
##  6 dimensional reductions calculated: RNA_pca, RNA_pca_20_tsne, RNA_pca_20_umap, harmony, harmony_20_umap, harmony_20_tsne

This is the projection name to visualize cells :

name2D = "harmony_20_tsne"

To represent results from differential expression, we load the analyses results :

list_results = readRDS(paste0(data_dir, "/4_zoom/1_zoom_immune/immune_cells_list_results.rds"))

lapply(list_results, FUN = names)
## $`Langerhans cells`
## [1] "mark" "gsea"
## 
## $macrophages
## [1] "mark"       "enrichr_hs" "enrichr_hd" "gsea"      
## 
## $`CD4 T cells`
## [1] "mark"       "enrichr_hs" "enrichr_hd" "gsea"      
## 
## $`CD8 T cells`
## [1] "mark"       "enrichr_hs" "enrichr_hd" "gsea"

Preparation

We defined cluster type and cluster family :

cluster_type = table(sobj_ic$cell_type, sobj_ic$seurat_clusters) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
                        levels(sobj_ic$cell_type)[cluster_type])

sobj_ic$cluster_type = cluster_type[sobj_ic$seurat_clusters]
sobj_ic$cluster_type = factor(sobj_ic$cluster_type,
                              levels = levels(sobj_ic$cell_type))
sobj_ic$cluster_family = custom_order_cell_type[sobj_ic$cluster_type, "cell_family"]
sobj_ic$cluster_family = factor(sobj_ic$cluster_family,
                                levels = names(family_color))

Figures

Control cells on the full atlas :

sobj$is_immune = (colnames(sobj) %in% colnames(sobj_ic))

Seurat::DimPlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                group.by = "is_immune", order = "TRUE") +
  ggplot2::scale_color_manual(values = c(family_color[["immune cells"]], bg_color),
                              breaks = c(TRUE, FALSE)) +
  ggplot2::labs(title = "Immune cells",
                subtitle = paste0(ncol(sobj_ic), " cells")) +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5, face = "bold"),
                 plot.subtitle = element_text(hjust = 0.5)) +
  Seurat::NoAxes() + Seurat::NoLegend()

Violin plot of IL1B in macrophages :

subsobj = subset(sobj_ic, seurat_clusters == 2)
table(subsobj$sample_type)
## 
##  HS  HD 
## 378  31
il1b_hs = subsobj@assays$RNA@data["IL1B", subsobj$sample_type == "HS"]
il1b_hd = subsobj@assays$RNA@data["IL1B", subsobj$sample_type == "HD"]
il1b_hs_VS_il1b_hd = stats::t.test(il1b_hs, il1b_hd)
il1b_hs_VS_il1b_hd
## 
##  Welch Two Sample t-test
## 
## data:  il1b_hs and il1b_hd
## t = 2.3206, df = 35.801, p-value = 0.02612
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.05066074 0.75429252
## sample estimates:
## mean of x mean of y 
## 0.9256690 0.5231924
Seurat::VlnPlot(subsobj, group.by = "sample_type",
                features = "IL1B", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

Split by sample :

Seurat::VlnPlot(subsobj, group.by = "sample_identifier",
                features = "IL1B", cols = sample_info$color) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

Violin plot of IL1B in macrophages :

il6_hs = subsobj@assays$RNA@data["IL6", subsobj$sample_type == "HS"]
il6_hd = subsobj@assays$RNA@data["IL6", subsobj$sample_type == "HD"]
il6_hs_VS_il6_hd = stats::t.test(il6_hs, il6_hd)
il6_hs_VS_il6_hd
## 
##  Welch Two Sample t-test
## 
## data:  il6_hs and il6_hd
## t = 2.3591, df = 377, p-value = 0.01883
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.001453521 0.016004661
## sample estimates:
##   mean of x   mean of y 
## 0.008729091 0.000000000
Seurat::VlnPlot(subsobj, group.by = "sample_type",
                features = "IL6", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

Violin plot of IL1B in macrophages :

subsobj = subset(sobj_ic, seurat_clusters == 2)
table(subsobj$sample_type)
## 
##  HS  HD 
## 378  31
tnf_hs = subsobj@assays$RNA@data["TNF", subsobj$sample_type == "HS"]
tnf_hd = subsobj@assays$RNA@data["TNF", subsobj$sample_type == "HD"]
tnf_hs_VS_tnf_hd = stats::t.test(tnf_hs, tnf_hd)
tnf_hs_VS_tnf_hd
## 
##  Welch Two Sample t-test
## 
## data:  tnf_hs and tnf_hd
## t = 3.8395, df = 45.546, p-value = 0.0003786
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.1140962 0.3657054
## sample estimates:
##  mean of x  mean of y 
## 0.31784960 0.07794879
Seurat::VlnPlot(subsobj, group.by = "sample_type",
                features = "TNF", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

Split by sample :

Seurat::VlnPlot(subsobj, group.by = "sample_identifier",
                features = "TNF", cols = sample_info$color) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

Violin plot of GZMA in CD4 T cells :

subsobj = subset(sobj_ic, seurat_clusters %in% c(0,10))
table(subsobj$sample_type)
## 
##  HS  HD 
## 569  90
gzma_hs = subsobj@assays$RNA@data["GZMA", subsobj$sample_type == "HS"]
gzma_hd = subsobj@assays$RNA@data["GZMA", subsobj$sample_type == "HD"]
gzma_hs_VS_gzma_hd = stats::t.test(gzma_hs, gzma_hd)
gzma_hs_VS_gzma_hd
## 
##  Welch Two Sample t-test
## 
## data:  gzma_hs and gzma_hd
## t = 14.755, df = 172.51, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  1.225578 1.604097
## sample estimates:
## mean of x mean of y 
## 1.8456108 0.4307735
Seurat::VlnPlot(subsobj, group.by = "sample_type",
                features = "GZMA", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

We represent some genes split by sample type :

plot_list = lapply(c("IL1B", "GZMA", "IFNG", "IL17A"), FUN = function(one_gene) {
  p = aquarius::plot_split_dimred(sobj_ic,
                                  reduction = name2D,
                                  split_by = "sample_type",
                                  color_by = one_gene,
                                  color_palette = c("gray70", "#FDBB84", "#EF6548", "#7F0000", "black"),
                                  main_pt_size = 0.6,
                                  bg_pt_size = 0.6,
                                  order = TRUE,
                                  bg_color = "gray95")
  p = patchwork::wrap_plots(p, nrow = 1) +
    patchwork::plot_layout(guides = "collect") +
    ggplot2::theme(legend.position = "right") &
    ggplot2::theme(plot.subtitle = element_blank())
  return(p)
})

patchwork::wrap_plots(plot_list, ncol = 2)

Barplot by cluster type :

quantif = table(sobj_ic$sample_identifier) %>%
  as.data.frame.table() %>%
  `colnames<-`(c("Sample", "nb_cells"))

aquarius::plot_barplot(df = table(sobj_ic$sample_identifier,
                                  sobj_ic$cluster_type) %>%
                         as.data.frame.table() %>%
                         `colnames<-`(c("Sample", "Cell Type", "Number")),
                       x = "Sample", y = "Number", fill = "Cell Type",
                       position = position_stack()) +
  ggplot2::geom_label(data = quantif, inherit.aes = FALSE,
                      aes(x = .data$Sample, y = 50 + .data$nb_cells, label = .data$nb_cells),
                      label.size = 0, size = 5) +
  ggplot2::scale_fill_manual(values = unlist(color_markers),
                             breaks = names(color_markers),
                             name = "Cell Type") +
  ggplot2::theme(axis.title.y = element_blank(),
                 axis.line.x = element_line(colour = "lightgray"),
                 text = element_text(size = 15),
                 legend.position = "none")

Dotplot of DE genes for macrophages :

subsobj = subset(sobj_ic, cluster_type == "macrophages")
features_oi = c("HLA-DQA2", "HLA-DPA1", "HLA-DRB5",
                "HLA-A", "HLA-C", "B2M",
                "C1QA", "C1QB", "C1QC")

Seurat::DotPlot(subsobj,
                assay = "RNA",
                features = features_oi,
                group.by = "sample_type",
                scale = FALSE) +
  ggplot2::coord_flip() +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  ggplot2::theme(axis.title.x = element_blank(),
                 axis.title.y = element_blank(),
                 axis.text.x = element_text(angle = 30, hjust = 1))

Heatmap for macrophages :

# Matrix
mat_expr = Seurat::GetAssayData(subsobj)
mat_expr = mat_expr[features_oi, ]
mat_expr = Matrix::t(mat_expr)
mat_expr = dynutils::scale_quantile(mat_expr) # between 0 and 1
mat_expr = Matrix::t(mat_expr)
dim(mat_expr) # genes x cells
## [1]   9 463
## Colors
list_colors = list()

# Heatmap
list_colors[["expression"]] = rev(RColorBrewer::brewer.pal(name = "RdBu", n = 9))

# Sample annotation (top annotation)
list_colors[["sample_type"]] = sample_type_colors
list_colors[["sample_identifier"]] = setNames(nm = sample_info$sample_identifier,
                                              sample_info$color)
# Cells order
column_order = subsobj@meta.data %>%
  dplyr::arrange(sample_type, sample_identifier) %>%
  rownames()
column_order = match(column_order, rownames(subsobj@meta.data))

# Heatmap
ha_top = HeatmapAnnotation(sample_type = subsobj$sample_type,
                           sample_identifier = subsobj$sample_identifier,
                           col = list(sample_type = list_colors[["sample_type"]],
                                      sample_identifier = list_colors[["sample_identifier"]]))

# Heatmap
ht = Heatmap(as.matrix(mat_expr),
             heatmap_legend_param = list(title = "Expression", at = c(0, 1), 
                                         labels = c("low", "high")),
             col = list_colors[["expression"]],
             top_annotation = ha_top,
             show_column_names = FALSE,
             column_order = column_order,
             column_gap = unit(2, "mm"),
             cluster_rows = FALSE,
             row_title = NULL,
             row_names_gp = grid::gpar(fontsize = 14, fontface = "plain"),
             use_raster = FALSE,
             show_heatmap_legend = TRUE,
             border = TRUE)

ComplexHeatmap::draw(ht,
                     merge_legend = TRUE,
                     heatmap_legend_side = "bottom",
                     annotation_legend_side = "bottom")

Dotplot of DE genes for CD4 T cells :

subsobj = subset(sobj_ic, cluster_type == "CD4 T cells")
# features_oi = c(
#   # HALLMARK_TNFA_SIGNALING_VIA_NFKB
#   "RIPK2", "KLF9", "PER1", "FOS", "BTG1", "PTGER4", "BIRC3", "AREG", "KLF6", "ZFP36", "NFKBIA",
#   # REACTOME_RHO_GTPASE_EFFECTORS
#   "CYBA", "ARPC3", "CFL1", "RPS27", "CALM1", "ACTB",
#   # GOBP_GRANZYME_MEDIATED_PROGRAMMED_CELL_DEATH_SIGNALING_PATHWAY
#   "BNIP3", "GZMA", "GZMB", "LAMP1", "NKG7", "SRGN", "UBE4B",
#   # GOBP_POSITIVE_REGULATION_OF_MEMORY_T_CELL_DIFFERENTIATION
#   "CD46", "HLA-DRA", "HLA-DRB1", "IL12RB1", "IL23A", "IL23R", "TNFSF4",
#   "KLRB1")
features_oi = c("GZMA", "KLRB1", "BTG1", "ZFP36", "NFKBIA", "TXNIP", "CXCR4")

Seurat::DotPlot(subsobj,
                assay = "RNA",
                features = features_oi,
                group.by = "sample_type",
                scale = FALSE) +
  ggplot2::coord_flip() +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  ggplot2::theme(axis.title.x = element_blank(),
                 axis.title.y = element_blank(),
                 axis.text.x = element_text(angle = 30, hjust = 1))

Heatmap for CD4 T cells :

# Matrix
mat_expr = Seurat::GetAssayData(subsobj)
mat_expr = mat_expr[features_oi, ]
mat_expr = Matrix::t(mat_expr)
mat_expr = dynutils::scale_quantile(mat_expr) # between 0 and 1
mat_expr = Matrix::t(mat_expr)
dim(mat_expr) # genes x cells
## [1]   7 848
## Colors
list_colors = list()

# Heatmap
list_colors[["expression"]] = rev(RColorBrewer::brewer.pal(name = "RdBu", n = 9))

# Sample annotation (top annotation)
list_colors[["sample_type"]] = sample_type_colors
list_colors[["sample_identifier"]] = setNames(nm = sample_info$sample_identifier,
                                              sample_info$color)
# Cells order
column_order = subsobj@meta.data %>%
  dplyr::arrange(sample_type, sample_identifier) %>%
  rownames()
column_order = match(column_order, rownames(subsobj@meta.data))

# Heatmap
ha_top = HeatmapAnnotation(sample_type = subsobj$sample_type,
                           sample_identifier = subsobj$sample_identifier,
                           col = list(sample_type = list_colors[["sample_type"]],
                                      sample_identifier = list_colors[["sample_identifier"]]))

# Heatmap
ht = Heatmap(as.matrix(mat_expr),
             heatmap_legend_param = list(title = "Expression", at = c(0, 1), 
                                         labels = c("low", "high")),
             col = list_colors[["expression"]],
             top_annotation = ha_top,
             show_column_names = FALSE,
             column_order = column_order,
             column_gap = unit(2, "mm"),
             cluster_rows = FALSE,
             row_title = NULL,
             row_names_gp = grid::gpar(fontsize = 14, fontface = "plain"),
             use_raster = FALSE,
             show_heatmap_legend = TRUE,
             border = TRUE)

ComplexHeatmap::draw(ht,
                     merge_legend = TRUE,
                     heatmap_legend_side = "bottom",
                     annotation_legend_side = "bottom")

HFSC

Settings

We load the HFSCs dataset :

sobj_hfsc = readRDS(paste0(data_dir, "/4_zoom/2_zoom_hfsc/hfsc_sobj.rds"))
sobj_hfsc
## An object of class Seurat 
## 15384 features across 1454 samples within 1 assay 
## Active assay: RNA (15384 features, 2000 variable features)
##  6 dimensional reductions calculated: RNA_pca, RNA_pca_24_tsne, RNA_pca_24_umap, harmony, harmony_24_umap, harmony_24_tsne

This is the projection name to visualize cells :

name2D = "harmony_24_tsne"

To represent results from differential expression, we load the analyses results :

list_results = readRDS(paste0(data_dir, "/4_zoom/2_zoom_hfsc/hfsc_list_results.rds"))

lapply(list_results, FUN = names)
## $cluster_0_8
## [1] "p_val"     "avg_logFC" "pct.1"     "pct.2"     "p_val_adj"
## 
## $cluster_2
## [1] "p_val"     "avg_logFC" "pct.1"     "pct.2"     "p_val_adj"
## 
## $cluster_1
## [1] "p_val"     "avg_logFC" "pct.1"     "pct.2"     "p_val_adj"
## 
## $cluster_3
## [1] "p_val"     "avg_logFC" "pct.1"     "pct.2"     "p_val_adj"

Figures

HFSCs on the full atlas :

sobj$is_hfsc = (colnames(sobj) %in% colnames(sobj_hfsc))

Seurat::DimPlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                group.by = "is_hfsc", order = "TRUE") +
  ggplot2::scale_color_manual(values = c(color_markers[["HFSC"]], bg_color),
                              breaks = c(TRUE, FALSE)) +
  ggplot2::labs(title = "HFSCs",
                subtitle = paste0(ncol(sobj_hfsc), " cells")) +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5, face = "bold"),
                 plot.subtitle = element_text(hjust = 0.5)) +
  Seurat::NoAxes() + Seurat::NoLegend()

KRT15 expression :

Seurat::FeaturePlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                    features = "KRT15") +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() + Seurat::NoLegend()

Genes of interest :

genes = c("TGFB2", "ANGPTL7", "FGF18", "MGP", "EPCAM", "KRT75", "NOTCH3", "PTHLH")

plot_list = lapply(c(1:length(genes)), FUN = function(gene_id) {
  gene = genes[[gene_id]]
  
  sobj_hfsc$my_gene = Seurat::FetchData(sobj_hfsc, gene)[, 1] %>%
    aquarius::run_rescale(., new_min = 0, new_max = 10)
  
  Seurat::FeaturePlot(sobj_hfsc, features = "my_gene", reduction = name2D) +
    ggplot2::scale_color_gradientn(colors = aquarius::color_gene,
                                   breaks = seq(0, 10, by = 2.5),
                                   labels = c("min", rep("", 3), "max")) +
    ggplot2::labs(title = gene) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5, size = 17),
                   plot.subtitle = element_text(hjust = 0.5, size = 15),
                   legend.text = element_text(size = 15),
                   legend.position = "none") +
    Seurat::NoAxes()
})

plot_list
## [[1]]

## 
## [[2]]

## 
## [[3]]

## 
## [[4]]

## 
## [[5]]

## 
## [[6]]

## 
## [[7]]

## 
## [[8]]

plot_list = lapply(c("IFITM3", "MIF", "DDIT4"), FUN = function(one_gene) {
  p = aquarius::plot_split_dimred(sobj_hfsc,
                                  reduction = name2D,
                                  split_by = "sample_type",
                                  color_by = one_gene,
                                  color_palette = viridis::viridis(n = 10),
                                  main_pt_size = 0.6,
                                  bg_pt_size = 0.6,
                                  order = TRUE,
                                  bg_color = "gray95")
  p = patchwork::wrap_plots(p, nrow = 1) +
    patchwork::plot_layout(guides = "collect") +
    ggplot2::theme(legend.position = "right") &
    ggplot2::theme(plot.subtitle = element_blank())
  return(p)
})

plot_list
## [[1]]

## 
## [[2]]

## 
## [[3]]

Project name :

# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj_hfsc), replace = FALSE, size = ncol(sobj_hfsc))

# Extract coordinates
cells_coord = sobj_hfsc@reductions[[name2D]]@cell.embeddings %>%
  as.data.frame() %>%
  `colnames<-`(c("Dim1", "Dim2"))
cells_coord$project_name = sobj_hfsc$project_name
cells_coord = cells_coord[(rnd_order), ]

# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = project_name)) +
  ggplot2::geom_point(size = 1.2) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  ggplot2::theme_void() +
  ggplot2::theme(aspect.ratio = 1,
                 legend.position = "none")

Cluster :

Seurat::DimPlot(sobj_hfsc, reduction = name2D, pt.size = 1,
                group.by = "seurat_clusters", label = TRUE) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()

Heatmap with proportions :

cluster_markers = c("TGFB2", "ANGPTL7", "EPCAM", "KRT75", "NOTCH3", "PTHLH",
                    "percent.mt", "log_nCount_RNA")

## Bottom annotation : gene expression by cluster
ht_annot = Seurat::FetchData(sobj_hfsc, slot = "data", vars = cluster_markers) %>%
  as.data.frame()
ht_annot$clusters = sobj_hfsc$seurat_clusters
ht_annot = ht_annot %>%
  dplyr::group_by(clusters) %>%
  dplyr::summarise_all(funs('mean' = mean)) %>%
  as.data.frame() %>%
  dplyr::select(-clusters) %>%
  `colnames<-`(c(cluster_markers))

ha_bottom = ComplexHeatmap::HeatmapAnnotation(df = ht_annot,
                                              which = "column",
                                              show_legend = TRUE,
                                              col = setNames(nm = cluster_markers,
                                                             lapply(cluster_markers, FUN = color_fun)),
                                              annotation_name_side = "left")

## Right annotation : number of cells by dataset
ht_annot = table(sobj_hfsc$sample_identifier) %>%
  as.data.frame.table() %>%
  `colnames<-`(c("sample_identifier", "nb_cells")) %>%
  `rownames<-`(.$sample_identifier) %>%
  dplyr::select(-sample_identifier)

ha_right = ComplexHeatmap::HeatmapAnnotation(
  df = ht_annot,
  which = "row",
  show_legend = TRUE,
  annotation_name_side = "bottom",
  col = list(nb_cells  = circlize::colorRamp2(colors = RColorBrewer::brewer.pal(name = "Greys", n = 9),
                                              breaks = seq(from = range(ht_annot$nb_cells)[1],
                                                           to = range(ht_annot$nb_cells)[2],
                                                           length.out = 9))))

## Heatmap
ht = aquarius::plot_prop_heatmap(df = sobj_hfsc@meta.data[, c("sample_identifier", "seurat_clusters")],
                                 bottom_annotation = ha_bottom,
                                 right_annotation = ha_right,
                                 cluster_rows = TRUE,
                                 column_names_centered = TRUE,
                                 prop_margin = 1,
                                 row_names_gp = grid::gpar(names = sample_info$sample_identifier,
                                                           col = sample_info$color,
                                                           fontface = "bold"),
                                 row_title = "Sample",
                                 column_title = "Cluster")

ComplexHeatmap::draw(ht,
                     merge_legend = TRUE,
                     heatmap_legend_side = "bottom")

Dotplot of DE genes in clusters 0 and 8, between HS and HD :

# features_oi = rownames(list_results$cluster_0_8)
# features_oi = features_oi[!grepl(features_oi, pattern = "^RP")]

subsobj = subset(sobj_hfsc, seurat_clusters %in% c(0,8))

features_oi = c("HLA-C", "HLA-B", "IFITM3", "MIF", "JUNB", "MARCKSL1",
                "LTBP2", "PDK4", "DDIT4", "S100A11")

Seurat::DotPlot(subsobj,
                assay = "RNA",
                features = features_oi,
                group.by = "sample_type",
                scale = FALSE) +
  ggplot2::coord_flip() +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  ggplot2::theme(axis.title.x = element_blank(),
                 axis.title.y = element_blank(),
                 axis.text.x = element_text(angle = 30, hjust = 1))

Heatmap for clulster 0 and 8 :

# Matrix
mat_expr = Seurat::GetAssayData(subsobj)
mat_expr = mat_expr[features_oi, ]
mat_expr = Matrix::t(mat_expr)
mat_expr = dynutils::scale_quantile(mat_expr) # between 0 and 1
mat_expr = Matrix::t(mat_expr)
dim(mat_expr) # genes x cells
## [1]  10 631
## Colors
list_colors = list()

# Heatmap
list_colors[["expression"]] = rev(RColorBrewer::brewer.pal(name = "RdBu", n = 9))

# Sample annotation (top annotation)
list_colors[["sample_type"]] = sample_type_colors
list_colors[["sample_identifier"]] = setNames(nm = sample_info$sample_identifier,
                                              sample_info$color)
# list_colors[["seurat_clusters"]] = setNames(aquarius::gg_color_hue(length(levels(subsobj$seurat_clusters))),
#                                             nm = levels(subsobj$seurat_clusters))
# Cells order
column_order = subsobj@meta.data %>%
  dplyr::arrange(sample_type, sample_identifier) %>%
  rownames()
column_order = match(column_order, rownames(subsobj@meta.data))

# Heatmap
ha_top = HeatmapAnnotation(sample_type = subsobj$sample_type,
                           sample_identifier = subsobj$sample_identifier,
                           # clusters = subsobj$seurat_clusters,
                           col = list(sample_type = list_colors[["sample_type"]],
                                      sample_identifier = list_colors[["sample_identifier"]]
                                      # clusters = list_colors[["seurat_clusters"]]
                           ))

# Heatmap
ht = Heatmap(as.matrix(mat_expr),
             heatmap_legend_param = list(title = "Expression", at = c(0, 1), 
                                         labels = c("low", "high")),
             col = list_colors[["expression"]],
             top_annotation = ha_top,
             show_column_names = FALSE,
             column_order = column_order,
             column_gap = unit(2, "mm"),
             cluster_rows = FALSE,
             row_title = NULL,
             row_names_gp = grid::gpar(fontsize = 14, fontface = "plain"),
             use_raster = FALSE,
             show_heatmap_legend = TRUE,
             border = TRUE)

ComplexHeatmap::draw(ht,
                     merge_legend = TRUE,
                     heatmap_legend_side = "bottom",
                     annotation_legend_side = "bottom")

Violin plot of IFITM3 :

table(subsobj$sample_type)
## 
##  HS  HD 
## 588  43
ifitm3_hs = subsobj@assays$RNA@data["IFITM3", subsobj$sample_type == "HS"]
ifitm3_hd = subsobj@assays$RNA@data["IFITM3", subsobj$sample_type == "HD"]
ifitm3_hs_VS_ifitm3_hd = stats::t.test(ifitm3_hs, ifitm3_hd)
ifitm3_hs_VS_ifitm3_hd
## 
##  Welch Two Sample t-test
## 
## data:  ifitm3_hs and ifitm3_hd
## t = 7.0204, df = 47.675, p-value = 7.081e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.5509680 0.9933324
## sample estimates:
## mean of x mean of y 
##  1.775647  1.003497
Seurat::VlnPlot(subsobj, group.by = "sample_type",
                features = "IFITM3", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

Violin plot of PDK4 :

table(subsobj$sample_type)
## 
##  HS  HD 
## 588  43
pdk4_hs = subsobj@assays$RNA@data["PDK4", subsobj$sample_type == "HS"]
pdk4_hd = subsobj@assays$RNA@data["PDK4", subsobj$sample_type == "HD"]
pdk4_hs_VS_pdk4_hd = stats::t.test(pdk4_hs, pdk4_hd)
pdk4_hs_VS_pdk4_hd
## 
##  Welch Two Sample t-test
## 
## data:  pdk4_hs and pdk4_hd
## t = -10.934, df = 43.02, p-value = 5.329e-14
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -2.523546 -1.737614
## sample estimates:
## mean of x mean of y 
## 0.1615592 2.2921393
Seurat::VlnPlot(subsobj, group.by = "sample_type",
                features = "PDK4", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

IBL and ORS

Settings

We load the IBL + ORS dataset :

sobj_iblors = readRDS(paste0(data_dir, "/4_zoom/3_zoom_iblmors/iblmors_sobj.rds"))
sobj_iblors
## An object of class Seurat 
## 16701 features across 3532 samples within 1 assay 
## Active assay: RNA (16701 features, 2000 variable features)
##  6 dimensional reductions calculated: RNA_pca, RNA_pca_20_tsne, RNA_pca_20_umap, harmony, harmony_20_umap, harmony_20_tsne

This is the projection name to visualize cells :

name2D = "harmony_20_tsne"

To represent results from differential expression, we load the analyses results :

list_results = readRDS(paste0(data_dir, "/4_zoom/3_zoom_iblmors/iblmors_list_results.rds"))

lapply(list_results, FUN = names)
## $IBL_vs_ORS
## [1] "mark"        "enrichr_ibl" "enrichr_ors" "gsea"       
## 
## $cluster5_vs_ORS
## [1] "mark"       "enrichr_up" "enrichr_dn" "gsea"      
## 
## $IBL_HS_vs_HD
## [1] "mark"       "enrichr_hs" "enrichr_hd" "gsea"      
## 
## $ORS_HS_vs_HD
## [1] "mark"       "enrichr_hs" "enrichr_hd" "gsea"

Preparation

We defined cluster type :

cluster_type = table(sobj_iblors$cell_type, sobj_iblors$seurat_clusters) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
                        levels(sobj_iblors$cell_type)[cluster_type])

sobj_iblors$cluster_type = cluster_type[sobj_iblors$seurat_clusters]
sobj_iblors$cluster_type = factor(sobj_iblors$cluster_type,
                                  levels = levels(sobj_iblors$cell_type))

Figures

IBL + ORS on the full atlas :

sobj$cell_bc = colnames(sobj)
sobj_iblors$cell_bc = colnames(sobj_iblors)
sobj$is_iblors = dplyr::left_join(sobj@meta.data[, c("cell_bc", "percent.mt")],
                                  sobj_iblors@meta.data[, c("cell_bc", "cluster_type")],
                                  by = "cell_bc")[, "cluster_type"]

Seurat::DimPlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                group.by = "is_iblors", order = "TRUE") +
  ggplot2::scale_color_manual(values = c(color_markers[c("IBL", "ORS")], bg_color),
                              breaks = c("IBL", "ORS", NA), na.value = bg_color) +
  ggplot2::labs(title = "IBL + ORS",
                subtitle = paste0(ncol(sobj_iblors), " cells")) +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5, face = "bold"),
                 plot.subtitle = element_text(hjust = 0.5)) +
  Seurat::NoAxes() + Seurat::NoLegend()

Project name :

# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj_iblors), replace = FALSE, size = ncol(sobj_iblors))

# Extract coordinates
cells_coord = sobj_iblors@reductions[[name2D]]@cell.embeddings %>%
  as.data.frame() %>%
  `colnames<-`(c("Dim1", "Dim2"))
cells_coord$project_name = sobj_iblors$project_name
cells_coord = cells_coord[(rnd_order), ]

# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = project_name)) +
  ggplot2::geom_point(size = 1.2) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  ggplot2::theme_void() +
  ggplot2::theme(aspect.ratio = 1,
                 legend.position = "none")

Cluster :

Seurat::DimPlot(sobj_iblors, reduction = name2D, pt.size = 1,
                group.by = "seurat_clusters", label = TRUE) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()

Cluster type :

Seurat::DimPlot(sobj_iblors, reduction = name2D, pt.size = 1,
                group.by = "cluster_type", cols = color_markers) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()

Cluster type split by sample type :

plot_list = aquarius::plot_split_dimred(sobj_iblors, reduction = name2D,
                                        group_by = "cluster_type",
                                        group_color = color_markers,
                                        split_by = "sample_type",
                                        bg_pt_size = 1, main_pt_size = 1,
                                        bg_color = bg_color)

patchwork::wrap_plots(plot_list) &
  Seurat::NoLegend()

DE genes between IBL and ORS :

mark = list_results$IBL_vs_ORS$mark
mark$gene_name = rownames(mark)
mark_label = rbind(
  # up-regulated in IBL
  mark %>% dplyr::top_n(., n = 20, wt = avg_logFC),
  # up-regulated in ORS
  mark %>% dplyr::top_n(., n = 20, wt = -avg_logFC),
  # representative and selective for IBL
  mark %>% dplyr::top_n(., n = 20, wt = (pct.1 - pct.2)),
  # representative and selective for ORS
  mark %>% dplyr::top_n(., n = 20, wt = -(pct.1 - pct.2))) %>%
  dplyr::distinct()
mark_label = mark_label[!grepl(rownames(mark_label), pattern = "^MT"), ]

ggplot2::ggplot(mark, aes(x = pct.1, y = pct.2, col = avg_logFC)) +
  ggplot2::geom_abline(slope = 1, intercept = 0, lty = 2) +
  ggplot2::geom_point() +
  ggrepel::geom_label_repel(data = mark_label, max.overlaps = Inf,
                            aes(x = pct.1, y = pct.2, label = gene_name),
                            col = "black", fill = NA, size = 3, label.size = NA) +
  ggplot2::labs(title = "Differentially expressed genes",
                subtitle = "between IBL and ORS") +
  ggplot2::scale_color_gradient2(low = aquarius::color_cnv[1],
                                 mid = aquarius::color_cnv[2],
                                 high = aquarius::color_cnv[3],
                                 midpoint = 0) +
  ggplot2::theme_classic() +
  ggplot2::theme(plot.title = element_text(hjust = 0.5),
                 plot.subtitle = element_text(hjust = 0.5))

GSEA for all genes, in IBL, between HS and HD :

# gs_oi = c("REACTOME_EXPORT_OF_VIRAL_RIBONUCLEOPROTEINS_FROM_NUCLEUS",
#           "GOBP_RESPONSE_TO_UV_C",
#           "GOBP_POSITIVE_REGULATION_OF_HISTONE_H3_K9_METHYLATION",
#           "GOBP_HISTONE_H3_K14_ACETYLATION",
#           "GOBP_MITOPHAGY")

list_results$IBL_HS_vs_HD$gsea@result %>%
  # dplyr::filter(ID %in% gs_oi) %>%
  dplyr::filter(pvalue <  0.05) %>%
  dplyr::top_n(., n = 200, wt = abs(NES)) %>%
  dplyr::mutate(too_long = ifelse(nchar(ID) > 60, yes = TRUE, no = FALSE)) %>%
  dplyr::mutate(ID = stringr::str_sub(ID, end = 60)) %>%
  dplyr::mutate(ID = ifelse(too_long, yes = paste0(ID, "..."), no = ID)) %>%
  aquarius::gsea_plot(show_legend = TRUE) +
  ggplot2::labs(title = "GSEA using all genes (count matrix)") +
  ggplot2::theme(plot.title = element_text(size = 20))

GSEA for all genes, in ORS, between HS and HD :

list_results$ORS_HS_vs_HD$gsea@result %>%
  # dplyr::filter(ID %in% gs_oi) %>%
  dplyr::filter(pvalue <  0.05) %>%
  dplyr::top_n(., n = 200, wt = abs(NES)) %>%
  dplyr::mutate(too_long = ifelse(nchar(ID) > 70, yes = TRUE, no = FALSE)) %>%
  dplyr::mutate(ID = stringr::str_sub(ID, end = 70)) %>%
  dplyr::mutate(ID = ifelse(too_long, yes = paste0(ID, "..."), no = ID)) %>%
  aquarius::gsea_plot(show_legend = TRUE) +
  ggplot2::labs(title = "GSEA using all genes (count matrix)") +
  ggplot2::theme(plot.title = element_text(size = 20))

Violin plot for IBL :

subsobj = subset(sobj_iblors, cluster_type == "IBL")

Seurat::VlnPlot(subsobj, group.by = "sample_type",
                features = c("DUSP1", "DDIT4", "MIF", "LGALS7", "ARF5", "S100A9"),
                cols = sample_type_colors, ncol = 3) &
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

Violin plot for ORS :

subsobj = subset(sobj_iblors, cluster_type == "ORS")

Seurat::VlnPlot(subsobj, group.by = "sample_type",
                features = c("DUSP1", "CLDN1", "KLF6", "CTGF", "CCL2", "S100A9", "IFI27", "IFITM3"),
                cols = sample_type_colors, ncol = 4) &
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

Split by sample :

Seurat::VlnPlot(subsobj, group.by = "sample_identifier",
                features = "IFI27", cols = sample_info$color) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")

Dotplot of DE genes in ORS, between cluster 5 and other ORS :

subsobj = subset(sobj_iblors, cluster_type == "ORS")
subsobj = subset(subsobj, seurat_clusters != 7)

# features_oi = list_results$cluster5_vs_ORS$mark %>%
#   dplyr::filter(p_val_adj < 0.05) %>%
#   dplyr::filter(abs(avg_logFC) > 0.5) %>%
#   dplyr::arrange(avg_logFC) %>%
#   rownames()

# features_oi = c("YBX3", "TXNIP", "KRT15", "NEAT1", "COL17A1", "FXYD3", "KRT14",
#                 "CXCL14", "MT2A", "MT1E", "FOS", "JUNB", "AQP3", "GLUL", "KLF5",
#                 "GSTM3", "ALDH3A1", "HLA-C", "LGALS7B", "SLC38A2", "EHF", "KLF3",
#                 "IL18", "CLEC2B", "IL20RB", "GPSM2", "DAAM1", "DUSP1", "KLF6",
#                 "ZFP36", "ATF3", "RHOB", "MT1X", "KLF4", "ETS2", "NFKBIZ", "ID1",
#                 "RNASET2", "HOPX", "WNT4", "POU3F1", "SPRY1", "AR", "THSD4", "PDGFC",
#                 "KRT31", "WFDC2", "SPINK5", "SLPI", "IL1R2", "PLAT", "TSC22D3",
#                 "KLF9", "WNT3", "FGFR3", "LAMB4", "IFI27", "DCN", "LY6D", "IGFBP3", "WFDC5",
#                 
#                 "S100A14", "TMSB10", "ACTG1", "ACTB", "CSTB", "APOE", "CALD1", "SOX4",
#                 "STMN1", "LMO4", "CEBPB", "TMEM45A", "CTSB", "GPX2", "C1QTNF12", "GJB6",
#                 "RBP1", "TPM1", "KRT17", "CALML3", "PTN", "DAPK2",
#                 "EGLN3", "FILIP1L", "FOXC1", "ADGRL3", "FST", "EFNB2", "SEMA5A",
#                 "FGFR1", "DACH1", "EGR2", "CLDN1", "CTGF", "DEFB1", "CARD18", "KRT6A",
#                 "S100A7", "KRT6B", "MGST1", "CHI3L1", "CCL19", "LGALS1", "CYP1B1", "VIM")

features_oi = c("YBX3", "TXNIP", "KRT14", "KRT15", "NEAT1",
                "FXYD3", "MT2A", "MT1E", "MT1X", "AQP3", "GLUL",
                # "HALLMARK_TNFA_SIGNALING_VIA_NFKB"
                "FOS", "JUNB", "DUSP1", "ZFP36", "NFKBIZ",
                "ATF3", "RHOB",  "ETS2", "IL18", "KLF4", "KLF6", "KLF9",
                "KLF3", "KLF5", "COL17A1", "THSD4", "WNT3", "WNT4", "SLPI", "PLAT",
                "LAMB4", "DCN", "SPINK5",
                "GSTM3", "ALDH3A1",  "LGALS7B", "SLC38A2", "EHF",  "CLEC2B",
                "IL20RB", "IL1R2", "IFI27", "CXCL14", "HLA-C", "GPSM2", "DAAM1",   "ID1",
                "RNASET2", "HOPX", "POU3F1", "SPRY1", "AR", "PDGFC",
                "WFDC2", "WFDC5", "TSC22D3", "FGFR3",  "LY6D", "IGFBP3", 
                
                # Other ORS
                "APOE", "CTSB", "CALD1", "SOX4",
                "STMN1", "LMO4", "CEBPB", "TMEM45A", "GPX2", "C1QTNF12", "GJB6",
                "KRT6A", "KRT17", "RBP1", "CALML3", "PTN", "DAPK2",
                "EGLN3", "FILIP1L", "ADGRL3", "FST", "EFNB2", "SEMA5A",
                "FGFR1", "EGR2", "CLDN1", "DEFB1", "CARD18", "MGST1")

Seurat::DotPlot(subsobj,
                assay = "RNA",
                features = features_oi,
                group.by = "sample_type",
                scale = FALSE) +
  ggplot2::coord_flip() +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  ggplot2::theme(axis.title.x = element_blank(),
                 axis.title.y = element_blank(),
                 axis.text.x = element_text(angle = 30, hjust = 1))

Heatmap for cluster 5 vs other ORS :

# Matrix
mat_expr = Seurat::GetAssayData(subsobj)
mat_expr = mat_expr[features_oi, ]
mat_expr = Matrix::t(mat_expr)
mat_expr = dynutils::scale_quantile(mat_expr) # between 0 and 1
mat_expr = Matrix::t(mat_expr)
dim(mat_expr) # genes x cells
## [1]   89 1793
## Colors
list_colors = list()
list_colors[["expression"]] = rev(RColorBrewer::brewer.pal(name = "RdBu", n = 9))
list_colors[["sample_type"]] = sample_type_colors
list_colors[["sample_identifier"]] = setNames(nm = sample_info$sample_identifier,
                                              sample_info$color)
list_colors[["seurat_clusters"]] = setNames(aquarius::gg_color_hue(length(levels(subsobj$seurat_clusters))),
                                            nm = levels(subsobj$seurat_clusters))
list_colors[["seurat_clusters"]] = list_colors[["seurat_clusters"]][unique(subsobj$seurat_clusters)]

# Cells order
column_order = subsobj@meta.data %>%
  dplyr::arrange(sample_type, sample_identifier) %>%
  rownames()
column_order = match(column_order, rownames(subsobj@meta.data))

# Annotation
ha_top = HeatmapAnnotation(sample_type = subsobj$sample_type,
                           sample_identifier = subsobj$sample_identifier,
                           clusters = subsobj$seurat_clusters,
                           col = list(sample_type = list_colors[["sample_type"]],
                                      sample_identifier = list_colors[["sample_identifier"]],
                                      clusters = list_colors[["seurat_clusters"]]))

# Heatmap
ht = Heatmap(as.matrix(mat_expr),
             heatmap_legend_param = list(title = "Expression", at = c(0, 1), 
                                         labels = c("low", "high")),
             col = list_colors[["expression"]],
             top_annotation = ha_top,
             # Cell grouping
             column_split = subsobj$sample_type %>% as.character(),
             cluster_columns = TRUE,
             column_title = NULL,
             show_column_dend = FALSE,
             show_column_names = FALSE,
             # Genes
             cluster_rows = FALSE,
             row_names_gp = grid::gpar(fontsize = 14, fontface = "plain"),
             # Style
             use_raster = FALSE,
             show_heatmap_legend = TRUE,
             border = TRUE)

ComplexHeatmap::draw(ht,
                     merge_legend = TRUE,
                     heatmap_legend_side = "bottom",
                     annotation_legend_side = "bottom")

Genes of interest :

genes = c("KRT16", "COL17A1", "DST", "KRT6B", "IL1R2", "WNT3",
          "IFI27", "CXCL14", "IGFBP3", "KRT15", "CD200")

plot_list = lapply(c(1:length(genes)), FUN = function(gene_id) {
  gene = genes[[gene_id]]
  
  sobj_iblors$my_gene = Seurat::FetchData(sobj_iblors, gene)[, 1] %>%
    aquarius::run_rescale(., new_min = 0, new_max = 10)
  
  Seurat::FeaturePlot(sobj_iblors, features = "my_gene", reduction = name2D, pt.size = 0.25) +
    ggplot2::scale_color_gradientn(colors = aquarius::color_gene,
                                   breaks = seq(0, 10, by = 2.5),
                                   labels = c("min", rep("", 3), "max")) +
    ggplot2::labs(title = gene) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5, size = 17),
                   plot.subtitle = element_text(hjust = 0.5, size = 15),
                   legend.text = element_text(size = 15),
                   legend.position = "none") +
    Seurat::NoAxes()
})

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HFSCs to IBL and ORS

Settings

We load the merged dataset :

sobj_traj = readRDS(paste0(data_dir, "/4_zoom/4_zoom_hfsc_iblmors/hfsc_iblmors_sobj_traj_tinga.rds"))
sobj_traj
## An object of class Seurat 
## 17050 features across 4986 samples within 1 assay 
## Active assay: RNA (17050 features, 2000 variable features)
##  8 dimensional reductions calculated: RNA_pca, RNA_pca_18_tsne, RNA_pca_18_umap, harmony, harmony_18_umap, harmony_18_tsne, harmony_dm, harmony_dm_5_umap

This is the projection name to visualize cells :

name2D = "harmony_dm"

We load the trajectory object for visualisation purpose :

my_traj = readRDS(paste0(data_dir, "/4_zoom/4_zoom_hfsc_iblmors/hfsc_iblmors_my_traj_tinga.rds"))
class(my_traj)
## [1] "dynwrap::with_dimred"     "dynwrap::with_trajectory"
## [3] "dynwrap::data_wrapper"    "list"

Preparation

We defined cell type based on individual object :

sobj_iblors$cell_bc = colnames(sobj_iblors)
sobj_traj$cell_bc = colnames(sobj_traj)
sobj_traj$cluster_type = dplyr::left_join(sobj_traj@meta.data[, c("cell_bc", "percent.mt")],
                                          sobj_iblors@meta.data[, c("cell_bc", "cluster_type")],
                                          by = "cell_bc")[, "cluster_type"] %>% as.character()
sobj_traj$cluster_type = ifelse(colnames(sobj_traj) %in% colnames(sobj_hfsc),
                                yes = "HFSC",
                                no = sobj_traj$cluster_type) %>%
  as.factor()

Figures

Cells on the full atlas :

sobj$cell_bc = colnames(sobj)
sobj_traj$cell_bc = colnames(sobj_traj)
sobj$is_traj = dplyr::left_join(sobj@meta.data[, c("cell_bc", "percent.mt")],
                                sobj_traj@meta.data[, c("cell_bc", "cluster_type")],
                                by = "cell_bc")[, "cluster_type"]

Seurat::DimPlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                group.by = "is_traj", order = levels(sobj_traj$cluster_type)) +
  ggplot2::scale_color_manual(values = c(color_markers[c("IBL", "ORS", "HFSC")], bg_color),
                              breaks = c("IBL", "ORS", "HFSC", NA), na.value = bg_color) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() + Seurat::NoLegend()

Project name :

# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj_traj), replace = FALSE, size = ncol(sobj_traj))

# Extract coordinates
cells_coord = sobj_traj@reductions[[name2D]]@cell.embeddings %>%
  as.data.frame() %>%
  `colnames<-`(c("Dim1", "Dim2"))
cells_coord$project_name = sobj_traj$project_name
cells_coord = cells_coord[(rnd_order), ]

# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = project_name)) +
  ggplot2::geom_point(size = 1.2) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  ggplot2::theme_void() +
  ggplot2::theme(aspect.ratio = 1,
                 legend.position = "none")

Cluster :

Seurat::DimPlot(sobj_traj, reduction = name2D, pt.size = 1,
                group.by = "seurat_clusters", label = TRUE) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()

Cluster type :

Seurat::DimPlot(sobj_traj, reduction = name2D, pt.size = 1,
                group.by = "cluster_type", cols = color_markers) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()

Pseudotime :

Seurat::FeaturePlot(sobj_traj, reduction = name2D, pt.size = 0.5,
                    features = "pseudotime") +
  ggplot2::scale_color_gradientn(colors = viridis::viridis(n = 100)) +
  ggplot2::lims(x = range(sobj_traj@reductions[[name2D]]@cell.embeddings[, 1]),
                y = range(sobj_traj@reductions[[name2D]]@cell.embeddings[, 2])) +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_blank()) +
  Seurat::NoAxes()

Pseudotime with dynplot’s function :

dynplot::plot_dimred(trajectory = my_traj,
                     dimred = sobj_traj[[name2D]]@cell.embeddings,
                     # Cells
                     color_cells = 'pseudotime',
                     size_cells = 1.6,
                     border_radius_percentage = 0,
                     # Trajectory
                     plot_trajectory = TRUE,
                     color_trajectory = "none",
                     label_milestones = FALSE,
                     size_milestones = 0,
                     size_transitions = 1)

R Session

show
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.6 LTS
## 
## Matrix products: default
## BLAS:   /usr/local/lib/R/lib/libRblas.so
## LAPACK: /usr/local/lib/R/lib/libRlapack.so
## 
## locale:
## [1] C
## 
## attached base packages:
##  [1] parallel  stats4    grid      stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] org.Mm.eg.db_3.10.0   AnnotationDbi_1.48.0  IRanges_2.20.2       
##  [4] S4Vectors_0.24.4      Biobase_2.46.0        BiocGenerics_0.32.0  
##  [7] ComplexHeatmap_2.14.0 ggplot2_3.3.5         patchwork_1.1.2      
## [10] dplyr_1.0.7          
## 
## loaded via a namespace (and not attached):
##   [1] softImpute_1.4              graphlayouts_0.7.0         
##   [3] pbapply_1.4-2               lattice_0.20-41            
##   [5] haven_2.3.1                 dyndimred_1.0.3            
##   [7] vctrs_0.3.8                 usethis_2.0.1              
##   [9] dynwrap_1.2.1               blob_1.2.1                 
##  [11] survival_3.2-13             prodlim_2019.11.13         
##  [13] dynutils_1.0.5              later_1.3.0                
##  [15] DBI_1.1.1                   R.utils_2.11.0             
##  [17] SingleCellExperiment_1.8.0  rappdirs_0.3.3             
##  [19] uwot_0.1.8                  dqrng_0.2.1                
##  [21] jpeg_0.1-8.1                zlibbioc_1.32.0            
##  [23] pspline_1.0-18              pcaMethods_1.78.0          
##  [25] mvtnorm_1.1-1               htmlwidgets_1.5.4          
##  [27] GlobalOptions_0.1.2         future_1.22.1              
##  [29] UpSetR_1.4.0                laeken_0.5.2               
##  [31] leiden_0.3.3                clustree_0.4.3             
##  [33] lmds_0.1.0                  scater_1.14.6              
##  [35] irlba_2.3.3                 DEoptimR_1.0-9             
##  [37] tidygraph_1.1.2             Rcpp_1.0.9                 
##  [39] readr_2.0.2                 KernSmooth_2.23-17         
##  [41] carrier_0.1.0               promises_1.1.0             
##  [43] gdata_2.18.0                DelayedArray_0.12.3        
##  [45] limma_3.42.2                graph_1.64.0               
##  [47] RcppParallel_5.1.4          Hmisc_4.4-0                
##  [49] fs_1.5.2                    RSpectra_0.16-0            
##  [51] fastmatch_1.1-0             ranger_0.12.1              
##  [53] digest_0.6.25               png_0.1-7                  
##  [55] sctransform_0.2.1           cowplot_1.0.0              
##  [57] DOSE_3.12.0                 here_1.0.1                 
##  [59] TInGa_0.0.0.9000            dynplot_1.1.0              
##  [61] ggraph_2.0.3                pkgconfig_2.0.3            
##  [63] GO.db_3.10.0                DelayedMatrixStats_1.8.0   
##  [65] gower_0.2.1                 ggbeeswarm_0.6.0           
##  [67] iterators_1.0.12            DropletUtils_1.6.1         
##  [69] reticulate_1.26             clusterProfiler_3.14.3     
##  [71] SummarizedExperiment_1.16.1 circlize_0.4.15            
##  [73] beeswarm_0.4.0              GetoptLong_1.0.5           
##  [75] xfun_0.35                   bslib_0.3.1                
##  [77] zoo_1.8-10                  tidyselect_1.1.0           
##  [79] GA_3.2                      reshape2_1.4.4             
##  [81] purrr_0.3.4                 ica_1.0-2                  
##  [83] pcaPP_1.9-73                viridisLite_0.3.0          
##  [85] rtracklayer_1.46.0          rlang_1.0.2                
##  [87] hexbin_1.28.1               jquerylib_0.1.4            
##  [89] dyneval_0.9.9               glue_1.4.2                 
##  [91] waldo_0.3.1                 RColorBrewer_1.1-2         
##  [93] matrixStats_0.56.0          stringr_1.4.0              
##  [95] lava_1.6.7                  europepmc_0.3              
##  [97] DESeq2_1.26.0               recipes_0.1.17             
##  [99] labeling_0.3                httpuv_1.5.2               
## [101] class_7.3-17                BiocNeighbors_1.4.2        
## [103] DO.db_2.9                   annotate_1.64.0            
## [105] jsonlite_1.7.2              XVector_0.26.0             
## [107] bit_4.0.4                   mime_0.9                   
## [109] aquarius_0.1.5              Rsamtools_2.2.3            
## [111] gridExtra_2.3               gplots_3.0.3               
## [113] stringi_1.4.6               processx_3.5.2             
## [115] gsl_2.1-6                   bitops_1.0-6               
## [117] cli_3.0.1                   batchelor_1.2.4            
## [119] RSQLite_2.2.0               randomForest_4.6-14        
## [121] tidyr_1.1.4                 data.table_1.14.2          
## [123] rstudioapi_0.13             GenomicAlignments_1.22.1   
## [125] nlme_3.1-147                qvalue_2.18.0              
## [127] scran_1.14.6                locfit_1.5-9.4             
## [129] scDblFinder_1.1.8           listenv_0.8.0              
## [131] ggthemes_4.2.4              gridGraphics_0.5-0         
## [133] R.oo_1.24.0                 dbplyr_1.4.4               
## [135] TTR_0.24.2                  readxl_1.3.1               
## [137] lifecycle_1.0.1             timeDate_3043.102          
## [139] ggpattern_0.3.1             munsell_0.5.0              
## [141] cellranger_1.1.0            R.methodsS3_1.8.1          
## [143] proxyC_0.1.5                visNetwork_2.0.9           
## [145] caTools_1.18.0              codetools_0.2-16           
## [147] GenomeInfoDb_1.22.1         vipor_0.4.5                
## [149] lmtest_0.9-38               msigdbr_7.5.1              
## [151] htmlTable_1.13.3            triebeard_0.3.0            
## [153] lsei_1.2-0                  xtable_1.8-4               
## [155] ROCR_1.0-7                  BiocManager_1.30.10        
## [157] scatterplot3d_0.3-41        abind_1.4-5                
## [159] farver_2.0.3                parallelly_1.28.1          
## [161] RANN_2.6.1                  askpass_1.1                
## [163] GenomicRanges_1.38.0        RcppAnnoy_0.0.16           
## [165] tibble_3.1.5                ggdendro_0.1-20            
## [167] cluster_2.1.0               future.apply_1.5.0         
## [169] Seurat_3.1.5                dendextend_1.15.1          
## [171] Matrix_1.3-2                ellipsis_0.3.2             
## [173] prettyunits_1.1.1           lubridate_1.7.9            
## [175] ggridges_0.5.2              igraph_1.2.5               
## [177] RcppEigen_0.3.3.7.0         fgsea_1.12.0               
## [179] remotes_2.4.2               scBFA_1.0.0                
## [181] destiny_3.0.1               VIM_6.1.1                  
## [183] testthat_3.1.0              htmltools_0.5.2            
## [185] BiocFileCache_1.10.2        yaml_2.2.1                 
## [187] utf8_1.1.4                  plotly_4.9.2.1             
## [189] XML_3.99-0.3                ModelMetrics_1.2.2.2       
## [191] e1071_1.7-3                 foreign_0.8-76             
## [193] withr_2.5.0                 fitdistrplus_1.0-14        
## [195] BiocParallel_1.20.1         xgboost_1.4.1.1            
## [197] bit64_4.0.5                 foreach_1.5.0              
## [199] robustbase_0.93-9           Biostrings_2.54.0          
## [201] GOSemSim_2.13.1             rsvd_1.0.3                 
## [203] memoise_2.0.0               evaluate_0.18              
## [205] forcats_0.5.0               rio_0.5.16                 
## [207] geneplotter_1.64.0          tzdb_0.1.2                 
## [209] caret_6.0-86                ps_1.6.0                   
## [211] DiagrammeR_1.0.6.1          curl_4.3                   
## [213] fdrtool_1.2.15              fansi_0.4.1                
## [215] highr_0.8                   urltools_1.7.3             
## [217] xts_0.12.1                  GSEABase_1.48.0            
## [219] acepack_1.4.1               edgeR_3.28.1               
## [221] checkmate_2.0.0             scds_1.2.0                 
## [223] cachem_1.0.6                npsurv_0.4-0               
## [225] babelgene_22.3              rjson_0.2.20               
## [227] openxlsx_4.1.5              ggrepel_0.9.1              
## [229] clue_0.3-60                 rprojroot_2.0.2            
## [231] stabledist_0.7-1            tools_3.6.3                
## [233] sass_0.4.0                  nichenetr_1.1.1            
## [235] magrittr_2.0.1              RCurl_1.98-1.2             
## [237] proxy_0.4-24                car_3.0-11                 
## [239] ape_5.3                     ggplotify_0.0.5            
## [241] xml2_1.3.2                  httr_1.4.2                 
## [243] assertthat_0.2.1            rmarkdown_2.18             
## [245] boot_1.3-25                 globals_0.14.0             
## [247] R6_2.4.1                    Rhdf5lib_1.8.0             
## [249] nnet_7.3-14                 RcppHNSW_0.2.0             
## [251] progress_1.2.2              genefilter_1.68.0          
## [253] statmod_1.4.34              gtools_3.8.2               
## [255] shape_1.4.6                 HDF5Array_1.14.4           
## [257] BiocSingular_1.2.2          rhdf5_2.30.1               
## [259] splines_3.6.3               AUCell_1.8.0               
## [261] carData_3.0-4               colorspace_1.4-1           
## [263] generics_0.1.0              base64enc_0.1-3            
## [265] dynfeature_1.0.0            smoother_1.1               
## [267] gridtext_0.1.1              pillar_1.6.3               
## [269] tweenr_1.0.1                sp_1.4-1                   
## [271] ggplot.multistats_1.0.0     rvcheck_0.1.8              
## [273] GenomeInfoDbData_1.2.2      plyr_1.8.6                 
## [275] gtable_0.3.0                zip_2.2.0                  
## [277] knitr_1.41                  latticeExtra_0.6-29        
## [279] biomaRt_2.42.1              fastmap_1.1.0              
## [281] ADGofTest_0.3               copula_1.0-0               
## [283] doParallel_1.0.15           vcd_1.4-8                  
## [285] babelwhale_1.0.1            openssl_1.4.1              
## [287] scales_1.1.1                backports_1.2.1            
## [289] ipred_0.9-12                enrichplot_1.6.1           
## [291] hms_1.1.1                   ggforce_0.3.1              
## [293] Rtsne_0.15                  shiny_1.7.1                
## [295] numDeriv_2016.8-1.1         polyclip_1.10-0            
## [297] lazyeval_0.2.2              Formula_1.2-3              
## [299] tsne_0.1-3                  crayon_1.3.4               
## [301] MASS_7.3-54                 pROC_1.16.2                
## [303] viridis_0.5.1               dynparam_1.0.0             
## [305] rpart_4.1-15                zinbwave_1.8.0             
## [307] compiler_3.6.3              ggtext_0.1.0
---
title: "HS project"
subtitle: "Figures"
author: "Audrey"
date: "`r format(Sys.time(), '%Y-%m-%d')`"
output:
  html_document:
    code_folding: show
    code_download: true
    toc: true
    toc_float: true
    number_sections: false
---

<style>
body {
text-align: justify}
</style>

<!-- Automatically computes and prints in the output the running time for any code chunk -->
```{r, echo=FALSE}
# https://github.com/rstudio/rmarkdown/issues/1453
hooks = knitr::knit_hooks$get()
hook_foldable = function(type) {
  force(type)
  function(x, options) {
    res = hooks[[type]](x, options)
    
    if (isFALSE(options[[paste0("fold_", type)]])) return(res)
    
    paste0(
      "<details><summary>", "show", "</summary>\n\n",
      res,
      "\n\n</details>"
    )
  }
}
knitr::knit_hooks$set(
  output = hook_foldable("output"),
  plot = hook_foldable("plot"),
  time_it = local({
    now = NULL
    function(before, options) {
      if (options$time_it) {
        if (before) {
          now <= Sys.time()
        } else {
          res = difftime(Sys.time(), now, units = "secs")
          paste("(Time to run :", round(res, digits = 2), "s)")
        }
      }
    }
  })
)
```

<!-- Set default parameters for all chunks -->
```{r, setup, include = FALSE}
set.seed(1337L)
knitr::opts_chunk$set(echo = TRUE, # display code
                      # display chunk output
                      message = FALSE,
                      warning = FALSE,
                      fold_output = FALSE, # useful for sessionInfo()
                      fold_plot = FALSE,
                      
                      # figure settings
                      fig.align = 'center',
                      fig.width = 20,
                      fig.height = 15,
                      
                      # something about seed, chunk and Rmarkdown compilation
                      # https://stackoverflow.com/questions/39417003/long-vectors-not-supported-yet-error-in-rmd-but-not-in-r-script
                      # cache = TRUE,
                      cache.lazy = FALSE, 
                      
                      # add runtime after chunk
                      time_it = FALSE,
                      
                      # save figures in PDF in a separate folder
                      dev = c('png', 'pdf'), # tiff or pdf alone renders bad in html
                      # dpi = 300,
                      fig.path = "figures_detail/",
                      pdf.options(encoding = "ISOLatin9.enc"))
```


This file is used to prepare the figures for the paper.

```{r library}
library(dplyr)
library(patchwork)
library(ggplot2)
library(ComplexHeatmap)
library(org.Mm.eg.db)

.libPaths()
```


# Preparation

Here are the folders where analyzes are stored :

```{r locations}
data_dir = "./.."
list.files(data_dir)
```


We load the dataset containing all cells :

```{r load_all_sobj}
sobj = readRDS(paste0(data_dir, "/3_combined/hs_hd_sobj.rds"))
sobj
```

These are all the samples analyzed :

```{r sample_info, class.source = 'fold-hide', fig.width = 8, fig.height = 3}
sample_info = readRDS(paste0(data_dir, "/1_metadata/hs_hd_sample_info.rds"))

# Nb cells by dataset
to_plot = table(sobj$sample_identifier) %>%
  as.data.frame.table(., stringsAsFactors = FALSE) %>%
  `colnames<-`(c("sample_identifier", "nb_cells")) %>%
  dplyr::left_join(x = ., y = sample_info, by = "sample_identifier") 

# patchwork
plot_list = aquarius::fig_plot_gb(to_plot, title = "Available datasets")
patchwork::wrap_plots(plot_list) +
  patchwork::plot_layout(design = "A\nB", heights = c(0.1,5)) &
  ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold", size = 15))
```

These are the custom colors for cell populations :

```{r color_markers, fig.width = 10, fig.height = 1, class.source = "fold-hide"}
color_markers = readRDS(paste0(data_dir, "/1_metadata/hs_hd_color_markers.rds"))
color_markers = color_markers[names(color_markers) != "melanocytes"]
ors_color = color_markers["ORS"]
color_markers["ORS"] = color_markers["IFE"] 
color_markers["IFE"] = ors_color
color_markers["B cells"] = "chocolate3"
rm(ors_color)

data.frame(cell_type = names(color_markers),
           color = unlist(color_markers)) %>%
  ggplot2::ggplot(., aes(x = cell_type, y = 0, fill = cell_type)) +
  ggplot2::geom_point(pch = 21, size = 5) +
  ggplot2::scale_fill_manual(values = unlist(color_markers), breaks = names(color_markers)) +
  ggplot2::theme_classic() +
  ggplot2::theme(legend.position = "none",
                 axis.line = element_blank(),
                 axis.title = element_blank(),
                 axis.ticks = element_blank(),
                 axis.text.y = element_blank(),
                 axis.text.x = element_text(hjust = 1, angle = 20))
```

We define custom colors for sample type :

```{r sample_type_colors, fig.width = 3, fig.height = 0.75, class.source = "fold-hide"}
sample_type_colors = setNames(nm = levels(sample_info$sample_type),
                              c("#C55F40", "#2C78E6"))

data.frame(cell_type = names(sample_type_colors),
           color = unlist(sample_type_colors)) %>%
  ggplot2::ggplot(., aes(x = cell_type, y = 0, fill = cell_type)) +
  ggplot2::geom_point(pch = 21, size = 5) +
  ggplot2::scale_fill_manual(values = unlist(sample_type_colors), breaks = names(sample_type_colors)) +
  ggplot2::theme_classic() +
  ggplot2::theme(legend.position = "none",
                 axis.line = element_blank(),
                 axis.title = element_blank(),
                 axis.ticks = element_blank(),
                 axis.text.y = element_blank())
```

We set a background color :

```{r bg_color}
bg_color = "gray94"
```


This is the correspondence between cell types and cell families, and custom colors to color cells by cell family :

```{r cell_family}
custom_order_cell_type = data.frame(
  cell_type = names(color_markers),
  cell_family = c(rep("immune cells", 5),
                  rep("matrix", 5),
                  rep("non matrix", 5)),
  stringsAsFactors = FALSE)
custom_order_cell_type$cell_type = factor(custom_order_cell_type$cell_type,
                                          levels = custom_order_cell_type$cell_type)
rownames(custom_order_cell_type) = custom_order_cell_type$cell_type

family_color = c("immune cells" = "slateblue1",
                 "matrix" = "mediumseagreen",
                 "non matrix" = "firebrick3")
```

We load markers to display on a dotplot to assess cell type annotation :

```{r dotplot_markers}
dotplot_markers = readRDS(paste0(data_dir, "/1_metadata/hs_hd_dotplot_markers.rds"))
dotplot_markers = dotplot_markers[names(dotplot_markers) != "melanocytes"]
lengths(dotplot_markers)
```

Custom functions to display gene expression on the heatmap :

```{r color_fun, class.source = "fold-hide"}
color_fun = function(one_gene) {
  gene_range = range(ht_annot[, one_gene])
  gene_palette = circlize::colorRamp2(colors = c("#FFFFFF", aquarius::color_gene[-1]),
                                      breaks = seq(from = gene_range[1], to = gene_range[2],
                                                   length.out = length(aquarius::color_gene)))
  return(gene_palette)
}
```


# All samples

## Settings

This is the projection name to visualize cells :

```{r all_sobj_name2D}
name2D = "harmony_38_tsne"
name2D_atlas = name2D
```

## Preparation

We make a low resolutive clustering for the heatmap :

```{r cluster_all}
sobj = Seurat::FindClusters(sobj, resolution = 0.4)

length(levels(sobj$seurat_clusters))
```


We define cluster type and cluster family :

```{r cluster_type_family_all, fig.width = 7, fig.height = 5}
cluster_type = table(sobj$cell_type, sobj$seurat_clusters) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
                        levels(sobj$cell_type)[cluster_type])

sobj$cluster_type = cluster_type[sobj$seurat_clusters]
sobj$cluster_type = factor(sobj$cluster_type,
                           levels = levels(sobj$cell_type))
sobj$cluster_family = custom_order_cell_type[sobj$cluster_type, "cell_family"]
sobj$cluster_family = factor(sobj$cluster_family,
                             levels = names(family_color))
```


## Figures

Project name :

```{r fig1_sample_identifier, fig.width = 4, fig.height = 4}
# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj), replace = FALSE, size = ncol(sobj))

# Extract coordinates
cells_coord = sobj@reductions[[name2D]]@cell.embeddings %>%
  as.data.frame() %>%
  `colnames<-`(c("Dim1", "Dim2"))
cells_coord$project_name = sobj$project_name
cells_coord = cells_coord[(rnd_order), ]

# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = project_name)) +
  ggplot2::geom_point(size = 0.5) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  ggplot2::theme_void() +
  ggplot2::theme(aspect.ratio = 1,
                 legend.position = "none")
```

Cluster :

```{r fig1_cluster, fig.width = 4, fig.height = 4}
Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.4,
                group.by = "seurat_clusters", label = TRUE) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()
```

Cell type annotation :

```{r fig1_cell_type, fig.width = 4, fig.height = 4}
Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
                group.by = "cell_type", cols = color_markers) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()
```

Cluster family annotation :

```{r fig1_cluster_family, fig.width = 6, fig.height = 6}
Seurat::DimPlot(sobj, reduction = name2D, pt.size = 0.5,
                group.by = "cluster_family", cols = family_color) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()
```

Cell type annotation split by condition :

```{r fig1_cell_type_split, fig.width = 8, fig.height = 5}
plot_list = aquarius::plot_split_dimred(sobj, reduction = name2D,
                                        group_by = "cell_type",
                                        group_color = color_markers,
                                        split_by = "sample_type",
                                        split_color = sample_type_colors,
                                        bg_color = bg_color)

patchwork::wrap_plots(plot_list) &
  Seurat::NoLegend()
```

Gene expression to assess annotation :

```{r fig1_gene_family, fig.width = 4, fig.height = 4}
genes = c("PTPRC", "MSX2", "KRT14")
names(genes) = c("immune cells", "matrix cells", "non-matrix cells")

plot_list = lapply(c(1:length(genes)), FUN = function(gene_id) {
  gene = genes[[gene_id]]
  pop = names(genes)[gene_id]
  
  sobj$my_gene = Seurat::FetchData(sobj, gene)[, 1] %>%
    aquarius::run_rescale(., new_min = 0, new_max = 10)
  
  Seurat::FeaturePlot(sobj, features = "my_gene", reduction = name2D) +
    ggplot2::scale_color_gradientn(colors = aquarius::color_gene,
                                   breaks = seq(0, 10, by = 2.5),
                                   labels = c("min", rep("", 3), "max")) +
    ggplot2::labs(title = gene) + 
    # subtitle = pop) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5, size = 17),
                   plot.subtitle = element_text(hjust = 0.5, size = 15),
                   legend.text = element_text(size = 15),
                   legend.position = "none") +
    Seurat::NoAxes()
})

plot_list
```

Barplot by cluster family :

```{r fig1_barplot_family, fig.width = 5.5, fig.height = 4.5}
quantif = table(sobj$sample_identifier) %>%
  as.data.frame.table() %>%
  `colnames<-`(c("Sample", "nb_cells"))

aquarius::plot_barplot(df = table(sobj$sample_identifier,
                                  sobj$cluster_family) %>%
                         as.data.frame.table() %>%
                         `colnames<-`(c("Sample", "Cell Type", "Number")),
                       x = "Sample", y = "Number", fill = "Cell Type",
                       position = position_fill()) +
  ggplot2::geom_label(data = quantif, inherit.aes = FALSE,
                      aes(x = .data$Sample, y = 1.05, label = .data$nb_cells),
                      label.size = 0, size = 5) +
  ggplot2::scale_fill_manual(values = unlist(family_color),
                             breaks = names(family_color),
                             name = "Cell Family") +
  ggplot2::scale_y_continuous(breaks = seq(0, 1, by = 0.25),
                              labels = paste0(seq(0, 100, by = 25), sep = " %"),
                              expand = ggplot2::expansion(add = c(0, 0.05))) +
  ggplot2::theme(axis.title.y = element_blank(),
                 axis.line.x = element_line(colour = "lightgray"),
                 text = element_text(size = 15),
                 legend.position = "none")
```

Heatmap of cluster proportion by sample :

```{r fig1_heatmap_prop, fig.width = 15, fig.height = 6, class.source = "fold-hide"}
group_by = "seurat_clusters"

cluster_by_sample = table(sobj$sample_identifier,
                          sobj@meta.data[, group_by]) %>%
  prop.table(margin = 1) %>%
  as.matrix()

## Representative markers
cluster_markers = c("PTPRC", "CD3E", "AIF1", "MSX2", "DIO2", "GPX2", "KRT16")
ht_annot = Seurat::GetAssayData(sobj, assay = "RNA", slot = "data")[cluster_markers, ] %>%
  Matrix::t() %>% as.data.frame()
ht_annot$clusters = sobj@meta.data[, group_by]
ht_annot = ht_annot %>%
  dplyr::group_by(clusters) %>%
  dplyr::summarise_all(funs('mean' = mean)) %>%
  as.data.frame() %>%
  dplyr::select(-clusters) %>%
  `colnames<-`(c(cluster_markers))

## Bottom annotation : average gene expression
ha_bottom = ComplexHeatmap::HeatmapAnnotation(df = ht_annot,
                                              which = "column",
                                              show_legend = FALSE,
                                              col = setNames(nm = cluster_markers,
                                                             lapply(cluster_markers, FUN = color_fun)),
                                              annotation_name_side = "right")

## Right annotation : number of cells by dataset
ht_annot = table(sobj$sample_identifier) %>%
  as.data.frame.table() %>%
  `colnames<-`(c("sample_identifier", "nb_cells")) %>%
  `rownames<-`(.$sample_identifier) %>%
  dplyr::select(-sample_identifier)

ha_right = ComplexHeatmap::HeatmapAnnotation(
  df = ht_annot,
  which = "row",
  show_legend = TRUE,
  annotation_name_side = "top",
  col = list(nb_cells  = circlize::colorRamp2(colors = RColorBrewer::brewer.pal(name = "Greys", n = 9),
                                              breaks = seq(from = range(ht_annot$nb_cells)[1],
                                                           to = range(ht_annot$nb_cells)[2],
                                                           length.out = 9))))

## Left annotation : gender
ha_left = ComplexHeatmap::HeatmapAnnotation(
  gender = sample_info$gender,
  which = "row",
  show_legend = TRUE,
  annotation_name_side = "bottom",
  col = list(gender = setNames(nm = c("F", "M"),
                               c("lightcyan3", "navyblue"))))

## Top annotation : main cell type in this cluster
ht_annot = table(sobj$sample_identifier,
                 sobj@meta.data[, group_by]) %>%
  prop.table(margin = 1) %>%
  as.matrix()

ht_annot = table(sobj$cell_type,
                 sobj@meta.data[, group_by]) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
ht_annot = data.frame(row.names = names(ht_annot),
                      cell_type = names(color_markers)[ht_annot],
                      stringsAsFactors = FALSE)
ht_annot = dplyr::left_join(ht_annot, custom_order_cell_type, by = "cell_type") %>%
  # Simplification for matrix
  dplyr::mutate(cell_type = ifelse(cell_type %in% c("medulla", "cortex", "cuticle"), yes = "hair shaft", no = cell_type)) %>%
  # Simplification for T cells
  dplyr::mutate(cell_type = ifelse(cell_type %in% c("CD4 T cells", "CD8 T cells"), yes = "T cells", no = cell_type)) %>%
  # Simplification for APC
  dplyr::mutate(cell_type = ifelse(cell_type %in% c("Langerhans cells", "macrophages"), yes = "APC", no = cell_type)) %>%
  # Add color
  dplyr::mutate(color = as.character(color_markers[cell_type])) %>%
  dplyr::mutate(color = ifelse(cell_type == "hair shaft", yes = "#FFB6C1", no = color)) %>%
  dplyr::mutate(color = ifelse(cell_type == "T cells", yes = "#8A6EE6", no = color)) %>%
  dplyr::mutate(color = ifelse(cell_type == "APC", yes = "#9CAA4B", no = color))

ha_top = ComplexHeatmap::HeatmapAnnotation(
  cell_type = ht_annot$cell_type,
  # cell_family = ht_annot$cell_family,
  which = "column",
  show_legend = TRUE,
  annotation_name_side = "left",
  col = list(cell_type = setNames(nm = ht_annot$cell_type,
                                  ht_annot$color)
             #, cell_family = family_color
  ))

## Assemble heatmap
ht = ComplexHeatmap::Heatmap(cluster_by_sample,
                             heatmap_legend_param = list(title = "Proportion",
                                                         col = c("#2166AC", "#F7F7F7", "#B2182B")),
                             # bottom_annotation = ha_bottom,
                             right_annotation = ha_right,
                             left_annotation = ha_left,
                             top_annotation = ha_top,
                             cluster_rows = TRUE,
                             cluster_columns = TRUE,
                             row_title = "Sample",
                             row_names_gp = grid::gpar(names = sample_info$sample_identifier,
                                                       col = sample_info$color,
                                                       fontface = "bold"),
                             column_title = "Cluster",
                             column_names_centered = TRUE,
                             row_names_side = "left",
                             column_names_side = "top",
                             column_names_rot = 0)

## Draw !
ComplexHeatmap::draw(ht, merge_legends = TRUE)
```

Dotplot :

```{r fig1_dotplot, fig.width = 6.5, fig.height = 7, class.source = "fold-hide"}
plot_list = aquarius::plot_dotplot(sobj,
                                   markers = c("PTPRC",
                                               "CD3E", "CD4",
                                               "CD3E", "CD8A",
                                               "CD207", "CPVL", 
                                               "TREM2", "MSR1",
                                               "CD79A", "CD79B",
                                               "KRT32", "KRT35",
                                               "KRT31", "PRR9",
                                               "BAMBI", "ADLH1A3",
                                               "KRT71", "KRT73",
                                               "TOP2A", "MCM5",
                                               "KRT16", "KRT6C",
                                               "KRT15", "GPX2",
                                               "SPINK5", "LY6D",
                                               "DIO2", "TCEAL2",
                                               "CLMP", "PPARG"),
                                   assay = "RNA", column_name = "cell_type", nb_hline = 0) +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  ggplot2::theme(legend.position = "right",
                 legend.box = "vertical",
                 axis.title = element_blank(),
                 axis.ticks.x = element_blank(),
                 axis.text.x = element_blank(),
                 axis.line.x = element_blank(),
                 plot.margin = unit(rep(0, 4), "cm"))

p = ggplot2::ggplot(custom_order_cell_type, aes(x = cell_type, y = 0)) +
  ggplot2::geom_point(size = 0) +
  ggplot2::geom_segment(aes(x = 0.5, xend = 5.5, y = 0, yend = 0), size = 6, col = family_color["immune cells"]) +
  ggplot2::geom_segment(aes(x = 5.5, xend = 10.5, y = 0, yend = 0), size = 6, col = family_color["matrix"]) +
  ggplot2::geom_segment(aes(x = 10.5, xend = 15.5, y = 0, yend = 0), size = 6, col = family_color["non matrix"]) +
  ggplot2::scale_y_continuous(expand = c(0,0), limits = c(0,0)) +
  ggplot2::theme_classic() +
  ggplot2::theme(axis.text.y = element_blank(),
                 axis.ticks.y = element_blank(),
                 axis.title = element_blank(),
                 axis.line.y = element_blank(),
                 axis.text.x = element_text(angle = 45, hjust = 1, size = 10, color = "black"),
                 plot.margin = unit(rep(0, 4), "cm"))

plot_list = patchwork::wrap_plots(plot_list, p,
                                  ncol = 1, heights = c(25, 1))
plot_list





```


# Immune cells

## Settings

We load the immune cells dataset :

```{r sobj_ic}
sobj_ic = readRDS(paste0(data_dir, "/4_zoom/1_zoom_immune/immune_cells_sobj.rds"))
sobj_ic
```


This is the projection name to visualize cells :

```{r sobj_name2D_ic}
name2D = "harmony_20_tsne"
```

To represent results from differential expression, we load the analyses results :

```{r list_results_ic}
list_results = readRDS(paste0(data_dir, "/4_zoom/1_zoom_immune/immune_cells_list_results.rds"))

lapply(list_results, FUN = names)
```


## Preparation

We defined cluster type and cluster family :

```{r cluster_type_family_ic, fig.width = 7, fig.height = 5}
cluster_type = table(sobj_ic$cell_type, sobj_ic$seurat_clusters) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
                        levels(sobj_ic$cell_type)[cluster_type])

sobj_ic$cluster_type = cluster_type[sobj_ic$seurat_clusters]
sobj_ic$cluster_type = factor(sobj_ic$cluster_type,
                              levels = levels(sobj_ic$cell_type))
sobj_ic$cluster_family = custom_order_cell_type[sobj_ic$cluster_type, "cell_family"]
sobj_ic$cluster_family = factor(sobj_ic$cluster_family,
                                levels = names(family_color))
```


## Figures

Control cells on the full atlas :

```{r fig_ic_location, fig.width = 2, fig.height = 2}
sobj$is_immune = (colnames(sobj) %in% colnames(sobj_ic))

Seurat::DimPlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                group.by = "is_immune", order = "TRUE") +
  ggplot2::scale_color_manual(values = c(family_color[["immune cells"]], bg_color),
                              breaks = c(TRUE, FALSE)) +
  ggplot2::labs(title = "Immune cells",
                subtitle = paste0(ncol(sobj_ic), " cells")) +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5, face = "bold"),
                 plot.subtitle = element_text(hjust = 0.5)) +
  Seurat::NoAxes() + Seurat::NoLegend()
```


Violin plot of IL1B in macrophages :

```{r fig_ic_mac_il1b, fig.width = 4, fig.height = 4}
subsobj = subset(sobj_ic, seurat_clusters == 2)
table(subsobj$sample_type)

il1b_hs = subsobj@assays$RNA@data["IL1B", subsobj$sample_type == "HS"]
il1b_hd = subsobj@assays$RNA@data["IL1B", subsobj$sample_type == "HD"]
il1b_hs_VS_il1b_hd = stats::t.test(il1b_hs, il1b_hd)
il1b_hs_VS_il1b_hd

Seurat::VlnPlot(subsobj, group.by = "sample_type",
                features = "IL1B", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```

Split by sample :

```{r fig_ic_mac_il1b_split, fig.width = 6, fig.height = 4}
Seurat::VlnPlot(subsobj, group.by = "sample_identifier",
                features = "IL1B", cols = sample_info$color) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```

Violin plot of IL1B in macrophages :

```{r fig_ic_mac_il6, fig.width = 4, fig.height = 4}
il6_hs = subsobj@assays$RNA@data["IL6", subsobj$sample_type == "HS"]
il6_hd = subsobj@assays$RNA@data["IL6", subsobj$sample_type == "HD"]
il6_hs_VS_il6_hd = stats::t.test(il6_hs, il6_hd)
il6_hs_VS_il6_hd

Seurat::VlnPlot(subsobj, group.by = "sample_type",
                features = "IL6", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```

Violin plot of IL1B in macrophages :

```{r fig_ic_mac_tnf, fig.width = 4, fig.height = 4}
subsobj = subset(sobj_ic, seurat_clusters == 2)
table(subsobj$sample_type)

tnf_hs = subsobj@assays$RNA@data["TNF", subsobj$sample_type == "HS"]
tnf_hd = subsobj@assays$RNA@data["TNF", subsobj$sample_type == "HD"]
tnf_hs_VS_tnf_hd = stats::t.test(tnf_hs, tnf_hd)
tnf_hs_VS_tnf_hd

Seurat::VlnPlot(subsobj, group.by = "sample_type",
                features = "TNF", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```

Split by sample :

```{r fig_ic_mac_tnf_split, fig.width = 6, fig.height = 4}
Seurat::VlnPlot(subsobj, group.by = "sample_identifier",
                features = "TNF", cols = sample_info$color) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```

Violin plot of GZMA in CD4 T cells :

```{r fig_ic_t4_gzma, fig.width = 4, fig.height = 4}
subsobj = subset(sobj_ic, seurat_clusters %in% c(0,10))
table(subsobj$sample_type)

gzma_hs = subsobj@assays$RNA@data["GZMA", subsobj$sample_type == "HS"]
gzma_hd = subsobj@assays$RNA@data["GZMA", subsobj$sample_type == "HD"]
gzma_hs_VS_gzma_hd = stats::t.test(gzma_hs, gzma_hd)
gzma_hs_VS_gzma_hd

Seurat::VlnPlot(subsobj, group.by = "sample_type",
                features = "GZMA", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```

We represent some genes split by sample type :

```{r fig_ic_gene_split, fig.width = 12, fig.height = 6}
plot_list = lapply(c("IL1B", "GZMA", "IFNG", "IL17A"), FUN = function(one_gene) {
  p = aquarius::plot_split_dimred(sobj_ic,
                                  reduction = name2D,
                                  split_by = "sample_type",
                                  color_by = one_gene,
                                  color_palette = c("gray70", "#FDBB84", "#EF6548", "#7F0000", "black"),
                                  main_pt_size = 0.6,
                                  bg_pt_size = 0.6,
                                  order = TRUE,
                                  bg_color = "gray95")
  p = patchwork::wrap_plots(p, nrow = 1) +
    patchwork::plot_layout(guides = "collect") +
    ggplot2::theme(legend.position = "right") &
    ggplot2::theme(plot.subtitle = element_blank())
  return(p)
})

patchwork::wrap_plots(plot_list, ncol = 2)
```

Barplot by cluster type :

```{r fig_ic_barplot, fig.width = 5.5, fig.height = 4.5}
quantif = table(sobj_ic$sample_identifier) %>%
  as.data.frame.table() %>%
  `colnames<-`(c("Sample", "nb_cells"))

aquarius::plot_barplot(df = table(sobj_ic$sample_identifier,
                                  sobj_ic$cluster_type) %>%
                         as.data.frame.table() %>%
                         `colnames<-`(c("Sample", "Cell Type", "Number")),
                       x = "Sample", y = "Number", fill = "Cell Type",
                       position = position_stack()) +
  ggplot2::geom_label(data = quantif, inherit.aes = FALSE,
                      aes(x = .data$Sample, y = 50 + .data$nb_cells, label = .data$nb_cells),
                      label.size = 0, size = 5) +
  ggplot2::scale_fill_manual(values = unlist(color_markers),
                             breaks = names(color_markers),
                             name = "Cell Type") +
  ggplot2::theme(axis.title.y = element_blank(),
                 axis.line.x = element_line(colour = "lightgray"),
                 text = element_text(size = 15),
                 legend.position = "none")
```

Dotplot of DE genes for macrophages :

```{r fig_ic_dotplot_macrophages, fig.height = 3.5, fig.width = 5}
subsobj = subset(sobj_ic, cluster_type == "macrophages")
features_oi = c("HLA-DQA2", "HLA-DPA1", "HLA-DRB5",
                "HLA-A", "HLA-C", "B2M",
                "C1QA", "C1QB", "C1QC")

Seurat::DotPlot(subsobj,
                assay = "RNA",
                features = features_oi,
                group.by = "sample_type",
                scale = FALSE) +
  ggplot2::coord_flip() +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  ggplot2::theme(axis.title.x = element_blank(),
                 axis.title.y = element_blank(),
                 axis.text.x = element_text(angle = 30, hjust = 1))
```

Heatmap for macrophages :

```{r fig_ic_heatmap_macrophages, fig.width = 5, fig.height = 4, class.source = "fold-hide"}
# Matrix
mat_expr = Seurat::GetAssayData(subsobj)
mat_expr = mat_expr[features_oi, ]
mat_expr = Matrix::t(mat_expr)
mat_expr = dynutils::scale_quantile(mat_expr) # between 0 and 1
mat_expr = Matrix::t(mat_expr)
dim(mat_expr) # genes x cells
## Colors
list_colors = list()

# Heatmap
list_colors[["expression"]] = rev(RColorBrewer::brewer.pal(name = "RdBu", n = 9))

# Sample annotation (top annotation)
list_colors[["sample_type"]] = sample_type_colors
list_colors[["sample_identifier"]] = setNames(nm = sample_info$sample_identifier,
                                              sample_info$color)
# Cells order
column_order = subsobj@meta.data %>%
  dplyr::arrange(sample_type, sample_identifier) %>%
  rownames()
column_order = match(column_order, rownames(subsobj@meta.data))

# Heatmap
ha_top = HeatmapAnnotation(sample_type = subsobj$sample_type,
                           sample_identifier = subsobj$sample_identifier,
                           col = list(sample_type = list_colors[["sample_type"]],
                                      sample_identifier = list_colors[["sample_identifier"]]))

# Heatmap
ht = Heatmap(as.matrix(mat_expr),
             heatmap_legend_param = list(title = "Expression", at = c(0, 1), 
                                         labels = c("low", "high")),
             col = list_colors[["expression"]],
             top_annotation = ha_top,
             show_column_names = FALSE,
             column_order = column_order,
             column_gap = unit(2, "mm"),
             cluster_rows = FALSE,
             row_title = NULL,
             row_names_gp = grid::gpar(fontsize = 14, fontface = "plain"),
             use_raster = FALSE,
             show_heatmap_legend = TRUE,
             border = TRUE)

ComplexHeatmap::draw(ht,
                     merge_legend = TRUE,
                     heatmap_legend_side = "bottom",
                     annotation_legend_side = "bottom")
```


Dotplot of DE genes for CD4 T cells :

```{r fig_ic_dotplot_cd4, fig.height = 3.5, fig.width = 5}
subsobj = subset(sobj_ic, cluster_type == "CD4 T cells")
# features_oi = c(
#   # HALLMARK_TNFA_SIGNALING_VIA_NFKB
#   "RIPK2", "KLF9", "PER1", "FOS", "BTG1", "PTGER4", "BIRC3", "AREG", "KLF6", "ZFP36", "NFKBIA",
#   # REACTOME_RHO_GTPASE_EFFECTORS
#   "CYBA", "ARPC3", "CFL1", "RPS27", "CALM1", "ACTB",
#   # GOBP_GRANZYME_MEDIATED_PROGRAMMED_CELL_DEATH_SIGNALING_PATHWAY
#   "BNIP3", "GZMA", "GZMB", "LAMP1", "NKG7", "SRGN", "UBE4B",
#   # GOBP_POSITIVE_REGULATION_OF_MEMORY_T_CELL_DIFFERENTIATION
#   "CD46", "HLA-DRA", "HLA-DRB1", "IL12RB1", "IL23A", "IL23R", "TNFSF4",
#   "KLRB1")
features_oi = c("GZMA", "KLRB1", "BTG1", "ZFP36", "NFKBIA", "TXNIP", "CXCR4")

Seurat::DotPlot(subsobj,
                assay = "RNA",
                features = features_oi,
                group.by = "sample_type",
                scale = FALSE) +
  ggplot2::coord_flip() +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  ggplot2::theme(axis.title.x = element_blank(),
                 axis.title.y = element_blank(),
                 axis.text.x = element_text(angle = 30, hjust = 1))
```

Heatmap for CD4 T cells :

```{r fig_ic_heatmap_cd4, fig.width = 5, fig.height = 4, class.source = "fold-hide"}
# Matrix
mat_expr = Seurat::GetAssayData(subsobj)
mat_expr = mat_expr[features_oi, ]
mat_expr = Matrix::t(mat_expr)
mat_expr = dynutils::scale_quantile(mat_expr) # between 0 and 1
mat_expr = Matrix::t(mat_expr)
dim(mat_expr) # genes x cells
## Colors
list_colors = list()

# Heatmap
list_colors[["expression"]] = rev(RColorBrewer::brewer.pal(name = "RdBu", n = 9))

# Sample annotation (top annotation)
list_colors[["sample_type"]] = sample_type_colors
list_colors[["sample_identifier"]] = setNames(nm = sample_info$sample_identifier,
                                              sample_info$color)
# Cells order
column_order = subsobj@meta.data %>%
  dplyr::arrange(sample_type, sample_identifier) %>%
  rownames()
column_order = match(column_order, rownames(subsobj@meta.data))

# Heatmap
ha_top = HeatmapAnnotation(sample_type = subsobj$sample_type,
                           sample_identifier = subsobj$sample_identifier,
                           col = list(sample_type = list_colors[["sample_type"]],
                                      sample_identifier = list_colors[["sample_identifier"]]))

# Heatmap
ht = Heatmap(as.matrix(mat_expr),
             heatmap_legend_param = list(title = "Expression", at = c(0, 1), 
                                         labels = c("low", "high")),
             col = list_colors[["expression"]],
             top_annotation = ha_top,
             show_column_names = FALSE,
             column_order = column_order,
             column_gap = unit(2, "mm"),
             cluster_rows = FALSE,
             row_title = NULL,
             row_names_gp = grid::gpar(fontsize = 14, fontface = "plain"),
             use_raster = FALSE,
             show_heatmap_legend = TRUE,
             border = TRUE)

ComplexHeatmap::draw(ht,
                     merge_legend = TRUE,
                     heatmap_legend_side = "bottom",
                     annotation_legend_side = "bottom")
```


# HFSC

## Settings

We load the HFSCs dataset :

```{r sobj_hfsc}
sobj_hfsc = readRDS(paste0(data_dir, "/4_zoom/2_zoom_hfsc/hfsc_sobj.rds"))
sobj_hfsc
```


This is the projection name to visualize cells :

```{r sobj_name2D_hfsc}
name2D = "harmony_24_tsne"
```

To represent results from differential expression, we load the analyses results :

```{r list_results_hfsc}
list_results = readRDS(paste0(data_dir, "/4_zoom/2_zoom_hfsc/hfsc_list_results.rds"))

lapply(list_results, FUN = names)
```

## Figures

HFSCs on the full atlas :

```{r fig_hfsc_location, fig.width = 2, fig.height = 2}
sobj$is_hfsc = (colnames(sobj) %in% colnames(sobj_hfsc))

Seurat::DimPlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                group.by = "is_hfsc", order = "TRUE") +
  ggplot2::scale_color_manual(values = c(color_markers[["HFSC"]], bg_color),
                              breaks = c(TRUE, FALSE)) +
  ggplot2::labs(title = "HFSCs",
                subtitle = paste0(ncol(sobj_hfsc), " cells")) +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5, face = "bold"),
                 plot.subtitle = element_text(hjust = 0.5)) +
  Seurat::NoAxes() + Seurat::NoLegend()
```

KRT15 expression :

```{r fig_hfsc_krt15, fig.width = 2, fig.height = 2}
Seurat::FeaturePlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                    features = "KRT15") +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() + Seurat::NoLegend()
```

Genes of interest :

```{r fig_hfsc_genes, fig.width = 4, fig.height = 4}
genes = c("TGFB2", "ANGPTL7", "FGF18", "MGP", "EPCAM", "KRT75", "NOTCH3", "PTHLH")

plot_list = lapply(c(1:length(genes)), FUN = function(gene_id) {
  gene = genes[[gene_id]]
  
  sobj_hfsc$my_gene = Seurat::FetchData(sobj_hfsc, gene)[, 1] %>%
    aquarius::run_rescale(., new_min = 0, new_max = 10)
  
  Seurat::FeaturePlot(sobj_hfsc, features = "my_gene", reduction = name2D) +
    ggplot2::scale_color_gradientn(colors = aquarius::color_gene,
                                   breaks = seq(0, 10, by = 2.5),
                                   labels = c("min", rep("", 3), "max")) +
    ggplot2::labs(title = gene) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5, size = 17),
                   plot.subtitle = element_text(hjust = 0.5, size = 15),
                   legend.text = element_text(size = 15),
                   legend.position = "none") +
    Seurat::NoAxes()
})

plot_list
```

```{r fig_hfsc_genes_split, fig.width = 7, fig.height = 4}
plot_list = lapply(c("IFITM3", "MIF", "DDIT4"), FUN = function(one_gene) {
  p = aquarius::plot_split_dimred(sobj_hfsc,
                                  reduction = name2D,
                                  split_by = "sample_type",
                                  color_by = one_gene,
                                  color_palette = viridis::viridis(n = 10),
                                  main_pt_size = 0.6,
                                  bg_pt_size = 0.6,
                                  order = TRUE,
                                  bg_color = "gray95")
  p = patchwork::wrap_plots(p, nrow = 1) +
    patchwork::plot_layout(guides = "collect") +
    ggplot2::theme(legend.position = "right") &
    ggplot2::theme(plot.subtitle = element_blank())
  return(p)
})

plot_list
```

Project name :

```{r fig_hfsc_project_name, fig.width = 4, fig.height = 4}
# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj_hfsc), replace = FALSE, size = ncol(sobj_hfsc))

# Extract coordinates
cells_coord = sobj_hfsc@reductions[[name2D]]@cell.embeddings %>%
  as.data.frame() %>%
  `colnames<-`(c("Dim1", "Dim2"))
cells_coord$project_name = sobj_hfsc$project_name
cells_coord = cells_coord[(rnd_order), ]

# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = project_name)) +
  ggplot2::geom_point(size = 1.2) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  ggplot2::theme_void() +
  ggplot2::theme(aspect.ratio = 1,
                 legend.position = "none")
```

Cluster :

```{r fig_hfsc_clusters, fig.width = 4, fig.height = 4}
Seurat::DimPlot(sobj_hfsc, reduction = name2D, pt.size = 1,
                group.by = "seurat_clusters", label = TRUE) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()
```

Heatmap with proportions :

```{r fig_hfsc_heatmap, fig.width = 4.5, fig.height = 7, class.source = "fold-hide"}
cluster_markers = c("TGFB2", "ANGPTL7", "EPCAM", "KRT75", "NOTCH3", "PTHLH",
                    "percent.mt", "log_nCount_RNA")

## Bottom annotation : gene expression by cluster
ht_annot = Seurat::FetchData(sobj_hfsc, slot = "data", vars = cluster_markers) %>%
  as.data.frame()
ht_annot$clusters = sobj_hfsc$seurat_clusters
ht_annot = ht_annot %>%
  dplyr::group_by(clusters) %>%
  dplyr::summarise_all(funs('mean' = mean)) %>%
  as.data.frame() %>%
  dplyr::select(-clusters) %>%
  `colnames<-`(c(cluster_markers))

ha_bottom = ComplexHeatmap::HeatmapAnnotation(df = ht_annot,
                                              which = "column",
                                              show_legend = TRUE,
                                              col = setNames(nm = cluster_markers,
                                                             lapply(cluster_markers, FUN = color_fun)),
                                              annotation_name_side = "left")

## Right annotation : number of cells by dataset
ht_annot = table(sobj_hfsc$sample_identifier) %>%
  as.data.frame.table() %>%
  `colnames<-`(c("sample_identifier", "nb_cells")) %>%
  `rownames<-`(.$sample_identifier) %>%
  dplyr::select(-sample_identifier)

ha_right = ComplexHeatmap::HeatmapAnnotation(
  df = ht_annot,
  which = "row",
  show_legend = TRUE,
  annotation_name_side = "bottom",
  col = list(nb_cells  = circlize::colorRamp2(colors = RColorBrewer::brewer.pal(name = "Greys", n = 9),
                                              breaks = seq(from = range(ht_annot$nb_cells)[1],
                                                           to = range(ht_annot$nb_cells)[2],
                                                           length.out = 9))))

## Heatmap
ht = aquarius::plot_prop_heatmap(df = sobj_hfsc@meta.data[, c("sample_identifier", "seurat_clusters")],
                                 bottom_annotation = ha_bottom,
                                 right_annotation = ha_right,
                                 cluster_rows = TRUE,
                                 column_names_centered = TRUE,
                                 prop_margin = 1,
                                 row_names_gp = grid::gpar(names = sample_info$sample_identifier,
                                                           col = sample_info$color,
                                                           fontface = "bold"),
                                 row_title = "Sample",
                                 column_title = "Cluster")

ComplexHeatmap::draw(ht,
                     merge_legend = TRUE,
                     heatmap_legend_side = "bottom")
```

Dotplot of DE genes in clusters 0 and 8, between HS and HD :

```{r fig_hfsc_dotplot, fig.height = 5, fig.width = 5}
# features_oi = rownames(list_results$cluster_0_8)
# features_oi = features_oi[!grepl(features_oi, pattern = "^RP")]

subsobj = subset(sobj_hfsc, seurat_clusters %in% c(0,8))

features_oi = c("HLA-C", "HLA-B", "IFITM3", "MIF", "JUNB", "MARCKSL1",
                "LTBP2", "PDK4", "DDIT4", "S100A11")

Seurat::DotPlot(subsobj,
                assay = "RNA",
                features = features_oi,
                group.by = "sample_type",
                scale = FALSE) +
  ggplot2::coord_flip() +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  ggplot2::theme(axis.title.x = element_blank(),
                 axis.title.y = element_blank(),
                 axis.text.x = element_text(angle = 30, hjust = 1))
```

Heatmap for clulster 0 and 8 :

```{r fig_hfsc_heatmap_cluster08, fig.width = 5, fig.height = 5, class.source = "fold-hide"}
# Matrix
mat_expr = Seurat::GetAssayData(subsobj)
mat_expr = mat_expr[features_oi, ]
mat_expr = Matrix::t(mat_expr)
mat_expr = dynutils::scale_quantile(mat_expr) # between 0 and 1
mat_expr = Matrix::t(mat_expr)
dim(mat_expr) # genes x cells
## Colors
list_colors = list()

# Heatmap
list_colors[["expression"]] = rev(RColorBrewer::brewer.pal(name = "RdBu", n = 9))

# Sample annotation (top annotation)
list_colors[["sample_type"]] = sample_type_colors
list_colors[["sample_identifier"]] = setNames(nm = sample_info$sample_identifier,
                                              sample_info$color)
# list_colors[["seurat_clusters"]] = setNames(aquarius::gg_color_hue(length(levels(subsobj$seurat_clusters))),
#                                             nm = levels(subsobj$seurat_clusters))
# Cells order
column_order = subsobj@meta.data %>%
  dplyr::arrange(sample_type, sample_identifier) %>%
  rownames()
column_order = match(column_order, rownames(subsobj@meta.data))

# Heatmap
ha_top = HeatmapAnnotation(sample_type = subsobj$sample_type,
                           sample_identifier = subsobj$sample_identifier,
                           # clusters = subsobj$seurat_clusters,
                           col = list(sample_type = list_colors[["sample_type"]],
                                      sample_identifier = list_colors[["sample_identifier"]]
                                      # clusters = list_colors[["seurat_clusters"]]
                           ))

# Heatmap
ht = Heatmap(as.matrix(mat_expr),
             heatmap_legend_param = list(title = "Expression", at = c(0, 1), 
                                         labels = c("low", "high")),
             col = list_colors[["expression"]],
             top_annotation = ha_top,
             show_column_names = FALSE,
             column_order = column_order,
             column_gap = unit(2, "mm"),
             cluster_rows = FALSE,
             row_title = NULL,
             row_names_gp = grid::gpar(fontsize = 14, fontface = "plain"),
             use_raster = FALSE,
             show_heatmap_legend = TRUE,
             border = TRUE)

ComplexHeatmap::draw(ht,
                     merge_legend = TRUE,
                     heatmap_legend_side = "bottom",
                     annotation_legend_side = "bottom")
```

Violin plot of IFITM3 :

```{r fig_hfsc_ifitm3, fig.width = 3, fig.height = 4}
table(subsobj$sample_type)

ifitm3_hs = subsobj@assays$RNA@data["IFITM3", subsobj$sample_type == "HS"]
ifitm3_hd = subsobj@assays$RNA@data["IFITM3", subsobj$sample_type == "HD"]
ifitm3_hs_VS_ifitm3_hd = stats::t.test(ifitm3_hs, ifitm3_hd)
ifitm3_hs_VS_ifitm3_hd

Seurat::VlnPlot(subsobj, group.by = "sample_type",
                features = "IFITM3", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```

Violin plot of PDK4 :

```{r fig_hfsc_pdk4, fig.width = 3, fig.height = 4}
table(subsobj$sample_type)

pdk4_hs = subsobj@assays$RNA@data["PDK4", subsobj$sample_type == "HS"]
pdk4_hd = subsobj@assays$RNA@data["PDK4", subsobj$sample_type == "HD"]
pdk4_hs_VS_pdk4_hd = stats::t.test(pdk4_hs, pdk4_hd)
pdk4_hs_VS_pdk4_hd

Seurat::VlnPlot(subsobj, group.by = "sample_type",
                features = "PDK4", cols = sample_type_colors) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```

# IBL and ORS

## Settings

We load the IBL + ORS dataset :

```{r sobj_iblors}
sobj_iblors = readRDS(paste0(data_dir, "/4_zoom/3_zoom_iblmors/iblmors_sobj.rds"))
sobj_iblors
```


This is the projection name to visualize cells :

```{r sobj_name2D_iblors}
name2D = "harmony_20_tsne"
```

To represent results from differential expression, we load the analyses results :

```{r list_results_iblors}
list_results = readRDS(paste0(data_dir, "/4_zoom/3_zoom_iblmors/iblmors_list_results.rds"))

lapply(list_results, FUN = names)
```

## Preparation

We defined cluster type :

```{r cluster_type_iblors, fig.width = 7, fig.height = 5}
cluster_type = table(sobj_iblors$cell_type, sobj_iblors$seurat_clusters) %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
cluster_type = setNames(nm = names(cluster_type),
                        levels(sobj_iblors$cell_type)[cluster_type])

sobj_iblors$cluster_type = cluster_type[sobj_iblors$seurat_clusters]
sobj_iblors$cluster_type = factor(sobj_iblors$cluster_type,
                                  levels = levels(sobj_iblors$cell_type))
```

## Figures

IBL + ORS on the full atlas :

```{r fig_iblors_location, fig.width = 2, fig.height = 2}
sobj$cell_bc = colnames(sobj)
sobj_iblors$cell_bc = colnames(sobj_iblors)
sobj$is_iblors = dplyr::left_join(sobj@meta.data[, c("cell_bc", "percent.mt")],
                                  sobj_iblors@meta.data[, c("cell_bc", "cluster_type")],
                                  by = "cell_bc")[, "cluster_type"]

Seurat::DimPlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                group.by = "is_iblors", order = "TRUE") +
  ggplot2::scale_color_manual(values = c(color_markers[c("IBL", "ORS")], bg_color),
                              breaks = c("IBL", "ORS", NA), na.value = bg_color) +
  ggplot2::labs(title = "IBL + ORS",
                subtitle = paste0(ncol(sobj_iblors), " cells")) +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5, face = "bold"),
                 plot.subtitle = element_text(hjust = 0.5)) +
  Seurat::NoAxes() + Seurat::NoLegend()
```


Project name :

```{r fig_iblors_project_name, fig.width = 4, fig.height = 4}
# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj_iblors), replace = FALSE, size = ncol(sobj_iblors))

# Extract coordinates
cells_coord = sobj_iblors@reductions[[name2D]]@cell.embeddings %>%
  as.data.frame() %>%
  `colnames<-`(c("Dim1", "Dim2"))
cells_coord$project_name = sobj_iblors$project_name
cells_coord = cells_coord[(rnd_order), ]

# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = project_name)) +
  ggplot2::geom_point(size = 1.2) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  ggplot2::theme_void() +
  ggplot2::theme(aspect.ratio = 1,
                 legend.position = "none")
```

Cluster :

```{r fig_iblors_clusters, fig.width = 4, fig.height = 4}
Seurat::DimPlot(sobj_iblors, reduction = name2D, pt.size = 1,
                group.by = "seurat_clusters", label = TRUE) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()
```

Cluster type :

```{r fig_iblors_cluster_type, fig.width = 4, fig.height = 4}
Seurat::DimPlot(sobj_iblors, reduction = name2D, pt.size = 1,
                group.by = "cluster_type", cols = color_markers) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()
```

Cluster type split by sample type :

```{r fig_iblors_cluster_type_split, fig.width = 8, fig.height = 4}
plot_list = aquarius::plot_split_dimred(sobj_iblors, reduction = name2D,
                                        group_by = "cluster_type",
                                        group_color = color_markers,
                                        split_by = "sample_type",
                                        bg_pt_size = 1, main_pt_size = 1,
                                        bg_color = bg_color)

patchwork::wrap_plots(plot_list) &
  Seurat::NoLegend()
```

DE genes between IBL and ORS :

```{r fig_iblors_de_pop, fig.width = 6, fig.height = 6}
mark = list_results$IBL_vs_ORS$mark
mark$gene_name = rownames(mark)
mark_label = rbind(
  # up-regulated in IBL
  mark %>% dplyr::top_n(., n = 20, wt = avg_logFC),
  # up-regulated in ORS
  mark %>% dplyr::top_n(., n = 20, wt = -avg_logFC),
  # representative and selective for IBL
  mark %>% dplyr::top_n(., n = 20, wt = (pct.1 - pct.2)),
  # representative and selective for ORS
  mark %>% dplyr::top_n(., n = 20, wt = -(pct.1 - pct.2))) %>%
  dplyr::distinct()
mark_label = mark_label[!grepl(rownames(mark_label), pattern = "^MT"), ]

ggplot2::ggplot(mark, aes(x = pct.1, y = pct.2, col = avg_logFC)) +
  ggplot2::geom_abline(slope = 1, intercept = 0, lty = 2) +
  ggplot2::geom_point() +
  ggrepel::geom_label_repel(data = mark_label, max.overlaps = Inf,
                            aes(x = pct.1, y = pct.2, label = gene_name),
                            col = "black", fill = NA, size = 3, label.size = NA) +
  ggplot2::labs(title = "Differentially expressed genes",
                subtitle = "between IBL and ORS") +
  ggplot2::scale_color_gradient2(low = aquarius::color_cnv[1],
                                 mid = aquarius::color_cnv[2],
                                 high = aquarius::color_cnv[3],
                                 midpoint = 0) +
  ggplot2::theme_classic() +
  ggplot2::theme(plot.title = element_text(hjust = 0.5),
                 plot.subtitle = element_text(hjust = 0.5))
```

GSEA for all genes, in IBL, between HS and HD :

```{r gsea_ibl, fig.width = 12, fig.height = 40, class.source = "fold-hide"}
# gs_oi = c("REACTOME_EXPORT_OF_VIRAL_RIBONUCLEOPROTEINS_FROM_NUCLEUS",
#           "GOBP_RESPONSE_TO_UV_C",
#           "GOBP_POSITIVE_REGULATION_OF_HISTONE_H3_K9_METHYLATION",
#           "GOBP_HISTONE_H3_K14_ACETYLATION",
#           "GOBP_MITOPHAGY")

list_results$IBL_HS_vs_HD$gsea@result %>%
  # dplyr::filter(ID %in% gs_oi) %>%
  dplyr::filter(pvalue <  0.05) %>%
  dplyr::top_n(., n = 200, wt = abs(NES)) %>%
  dplyr::mutate(too_long = ifelse(nchar(ID) > 60, yes = TRUE, no = FALSE)) %>%
  dplyr::mutate(ID = stringr::str_sub(ID, end = 60)) %>%
  dplyr::mutate(ID = ifelse(too_long, yes = paste0(ID, "..."), no = ID)) %>%
  aquarius::gsea_plot(show_legend = TRUE) +
  ggplot2::labs(title = "GSEA using all genes (count matrix)") +
  ggplot2::theme(plot.title = element_text(size = 20))
```


GSEA for all genes, in ORS, between HS and HD :

```{r gsea_ors, fig.width = 12, fig.height = 40, class.source = "fold-hide"}
list_results$ORS_HS_vs_HD$gsea@result %>%
  # dplyr::filter(ID %in% gs_oi) %>%
  dplyr::filter(pvalue <  0.05) %>%
  dplyr::top_n(., n = 200, wt = abs(NES)) %>%
  dplyr::mutate(too_long = ifelse(nchar(ID) > 70, yes = TRUE, no = FALSE)) %>%
  dplyr::mutate(ID = stringr::str_sub(ID, end = 70)) %>%
  dplyr::mutate(ID = ifelse(too_long, yes = paste0(ID, "..."), no = ID)) %>%
  aquarius::gsea_plot(show_legend = TRUE) +
  ggplot2::labs(title = "GSEA using all genes (count matrix)") +
  ggplot2::theme(plot.title = element_text(size = 20))
```

Violin plot for IBL :

```{r fig_iblors_ibl, fig.width = 10, fig.height = 8}
subsobj = subset(sobj_iblors, cluster_type == "IBL")

Seurat::VlnPlot(subsobj, group.by = "sample_type",
                features = c("DUSP1", "DDIT4", "MIF", "LGALS7", "ARF5", "S100A9"),
                cols = sample_type_colors, ncol = 3) &
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```

Violin plot for ORS :

```{r fig_iblors_ors, fig.width = 12, fig.height = 8}
subsobj = subset(sobj_iblors, cluster_type == "ORS")

Seurat::VlnPlot(subsobj, group.by = "sample_type",
                features = c("DUSP1", "CLDN1", "KLF6", "CTGF", "CCL2", "S100A9", "IFI27", "IFITM3"),
                cols = sample_type_colors, ncol = 4) &
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```

Split by sample :

```{r fig_iblors_ors_split, fig.width = 6, fig.height = 4}
Seurat::VlnPlot(subsobj, group.by = "sample_identifier",
                features = "IFI27", cols = sample_info$color) +
  ggplot2::theme(axis.title.x = element_blank(),
                 legend.position = "none")
```




Dotplot of DE genes in ORS, between cluster 5 and other ORS :

```{r fig_iblors_dotplot_cluster5, fig.height = 20, fig.width = 5, class.source = "fold-hide"}
subsobj = subset(sobj_iblors, cluster_type == "ORS")
subsobj = subset(subsobj, seurat_clusters != 7)

# features_oi = list_results$cluster5_vs_ORS$mark %>%
#   dplyr::filter(p_val_adj < 0.05) %>%
#   dplyr::filter(abs(avg_logFC) > 0.5) %>%
#   dplyr::arrange(avg_logFC) %>%
#   rownames()

# features_oi = c("YBX3", "TXNIP", "KRT15", "NEAT1", "COL17A1", "FXYD3", "KRT14",
#                 "CXCL14", "MT2A", "MT1E", "FOS", "JUNB", "AQP3", "GLUL", "KLF5",
#                 "GSTM3", "ALDH3A1", "HLA-C", "LGALS7B", "SLC38A2", "EHF", "KLF3",
#                 "IL18", "CLEC2B", "IL20RB", "GPSM2", "DAAM1", "DUSP1", "KLF6",
#                 "ZFP36", "ATF3", "RHOB", "MT1X", "KLF4", "ETS2", "NFKBIZ", "ID1",
#                 "RNASET2", "HOPX", "WNT4", "POU3F1", "SPRY1", "AR", "THSD4", "PDGFC",
#                 "KRT31", "WFDC2", "SPINK5", "SLPI", "IL1R2", "PLAT", "TSC22D3",
#                 "KLF9", "WNT3", "FGFR3", "LAMB4", "IFI27", "DCN", "LY6D", "IGFBP3", "WFDC5",
#                 
#                 "S100A14", "TMSB10", "ACTG1", "ACTB", "CSTB", "APOE", "CALD1", "SOX4",
#                 "STMN1", "LMO4", "CEBPB", "TMEM45A", "CTSB", "GPX2", "C1QTNF12", "GJB6",
#                 "RBP1", "TPM1", "KRT17", "CALML3", "PTN", "DAPK2",
#                 "EGLN3", "FILIP1L", "FOXC1", "ADGRL3", "FST", "EFNB2", "SEMA5A",
#                 "FGFR1", "DACH1", "EGR2", "CLDN1", "CTGF", "DEFB1", "CARD18", "KRT6A",
#                 "S100A7", "KRT6B", "MGST1", "CHI3L1", "CCL19", "LGALS1", "CYP1B1", "VIM")

features_oi = c("YBX3", "TXNIP", "KRT14", "KRT15", "NEAT1",
                "FXYD3", "MT2A", "MT1E", "MT1X", "AQP3", "GLUL",
                # "HALLMARK_TNFA_SIGNALING_VIA_NFKB"
                "FOS", "JUNB", "DUSP1", "ZFP36", "NFKBIZ",
                "ATF3", "RHOB",  "ETS2", "IL18", "KLF4", "KLF6", "KLF9",
                "KLF3", "KLF5", "COL17A1", "THSD4", "WNT3", "WNT4", "SLPI", "PLAT",
                "LAMB4", "DCN", "SPINK5",
                "GSTM3", "ALDH3A1",  "LGALS7B", "SLC38A2", "EHF",  "CLEC2B",
                "IL20RB", "IL1R2", "IFI27", "CXCL14", "HLA-C", "GPSM2", "DAAM1",   "ID1",
                "RNASET2", "HOPX", "POU3F1", "SPRY1", "AR", "PDGFC",
                "WFDC2", "WFDC5", "TSC22D3", "FGFR3",  "LY6D", "IGFBP3", 
                
                # Other ORS
                "APOE", "CTSB", "CALD1", "SOX4",
                "STMN1", "LMO4", "CEBPB", "TMEM45A", "GPX2", "C1QTNF12", "GJB6",
                "KRT6A", "KRT17", "RBP1", "CALML3", "PTN", "DAPK2",
                "EGLN3", "FILIP1L", "ADGRL3", "FST", "EFNB2", "SEMA5A",
                "FGFR1", "EGR2", "CLDN1", "DEFB1", "CARD18", "MGST1")

Seurat::DotPlot(subsobj,
                assay = "RNA",
                features = features_oi,
                group.by = "sample_type",
                scale = FALSE) +
  ggplot2::coord_flip() +
  ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
  ggplot2::theme(axis.title.x = element_blank(),
                 axis.title.y = element_blank(),
                 axis.text.x = element_text(angle = 30, hjust = 1))
```

Heatmap for cluster 5 vs other ORS :

```{r fig_iblors_heatmap_cluster5, fig.width = 12, fig.height = 19, class.source = "fold-hide"}
# Matrix
mat_expr = Seurat::GetAssayData(subsobj)
mat_expr = mat_expr[features_oi, ]
mat_expr = Matrix::t(mat_expr)
mat_expr = dynutils::scale_quantile(mat_expr) # between 0 and 1
mat_expr = Matrix::t(mat_expr)
dim(mat_expr) # genes x cells

## Colors
list_colors = list()
list_colors[["expression"]] = rev(RColorBrewer::brewer.pal(name = "RdBu", n = 9))
list_colors[["sample_type"]] = sample_type_colors
list_colors[["sample_identifier"]] = setNames(nm = sample_info$sample_identifier,
                                              sample_info$color)
list_colors[["seurat_clusters"]] = setNames(aquarius::gg_color_hue(length(levels(subsobj$seurat_clusters))),
                                            nm = levels(subsobj$seurat_clusters))
list_colors[["seurat_clusters"]] = list_colors[["seurat_clusters"]][unique(subsobj$seurat_clusters)]

# Cells order
column_order = subsobj@meta.data %>%
  dplyr::arrange(sample_type, sample_identifier) %>%
  rownames()
column_order = match(column_order, rownames(subsobj@meta.data))

# Annotation
ha_top = HeatmapAnnotation(sample_type = subsobj$sample_type,
                           sample_identifier = subsobj$sample_identifier,
                           clusters = subsobj$seurat_clusters,
                           col = list(sample_type = list_colors[["sample_type"]],
                                      sample_identifier = list_colors[["sample_identifier"]],
                                      clusters = list_colors[["seurat_clusters"]]))

# Heatmap
ht = Heatmap(as.matrix(mat_expr),
             heatmap_legend_param = list(title = "Expression", at = c(0, 1), 
                                         labels = c("low", "high")),
             col = list_colors[["expression"]],
             top_annotation = ha_top,
             # Cell grouping
             column_split = subsobj$sample_type %>% as.character(),
             cluster_columns = TRUE,
             column_title = NULL,
             show_column_dend = FALSE,
             show_column_names = FALSE,
             # Genes
             cluster_rows = FALSE,
             row_names_gp = grid::gpar(fontsize = 14, fontface = "plain"),
             # Style
             use_raster = FALSE,
             show_heatmap_legend = TRUE,
             border = TRUE)

ComplexHeatmap::draw(ht,
                     merge_legend = TRUE,
                     heatmap_legend_side = "bottom",
                     annotation_legend_side = "bottom")
```

Genes of interest :

```{r fig_iblors_genes, fig.width = 2, fig.height = 2}
genes = c("KRT16", "COL17A1", "DST", "KRT6B", "IL1R2", "WNT3",
          "IFI27", "CXCL14", "IGFBP3", "KRT15", "CD200")

plot_list = lapply(c(1:length(genes)), FUN = function(gene_id) {
  gene = genes[[gene_id]]
  
  sobj_iblors$my_gene = Seurat::FetchData(sobj_iblors, gene)[, 1] %>%
    aquarius::run_rescale(., new_min = 0, new_max = 10)
  
  Seurat::FeaturePlot(sobj_iblors, features = "my_gene", reduction = name2D, pt.size = 0.25) +
    ggplot2::scale_color_gradientn(colors = aquarius::color_gene,
                                   breaks = seq(0, 10, by = 2.5),
                                   labels = c("min", rep("", 3), "max")) +
    ggplot2::labs(title = gene) +
    ggplot2::theme(aspect.ratio = 1,
                   plot.title = element_text(hjust = 0.5, size = 17),
                   plot.subtitle = element_text(hjust = 0.5, size = 15),
                   legend.text = element_text(size = 15),
                   legend.position = "none") +
    Seurat::NoAxes()
})

plot_list
```


# HFSCs to IBL and ORS

## Settings

We load the merged dataset :

```{r sobj_traj}
sobj_traj = readRDS(paste0(data_dir, "/4_zoom/4_zoom_hfsc_iblmors/hfsc_iblmors_sobj_traj_tinga.rds"))
sobj_traj
```


This is the projection name to visualize cells :

```{r sobj_name2D_traj}
name2D = "harmony_dm"
```

We load the trajectory object for visualisation purpose :

```{r load_traj}
my_traj = readRDS(paste0(data_dir, "/4_zoom/4_zoom_hfsc_iblmors/hfsc_iblmors_my_traj_tinga.rds"))
class(my_traj)
```


## Preparation

We defined cell type based on individual object :

```{r cluster_type_traj, fig.width = 7, fig.height = 5}
sobj_iblors$cell_bc = colnames(sobj_iblors)
sobj_traj$cell_bc = colnames(sobj_traj)
sobj_traj$cluster_type = dplyr::left_join(sobj_traj@meta.data[, c("cell_bc", "percent.mt")],
                                          sobj_iblors@meta.data[, c("cell_bc", "cluster_type")],
                                          by = "cell_bc")[, "cluster_type"] %>% as.character()
sobj_traj$cluster_type = ifelse(colnames(sobj_traj) %in% colnames(sobj_hfsc),
                                yes = "HFSC",
                                no = sobj_traj$cluster_type) %>%
  as.factor()
```

## Figures

Cells on the full atlas :

```{r fig_traj_location, fig.width = 2, fig.height = 2}
sobj$cell_bc = colnames(sobj)
sobj_traj$cell_bc = colnames(sobj_traj)
sobj$is_traj = dplyr::left_join(sobj@meta.data[, c("cell_bc", "percent.mt")],
                                sobj_traj@meta.data[, c("cell_bc", "cluster_type")],
                                by = "cell_bc")[, "cluster_type"]

Seurat::DimPlot(sobj, reduction = name2D_atlas, pt.size = 0.000001,
                group.by = "is_traj", order = levels(sobj_traj$cluster_type)) +
  ggplot2::scale_color_manual(values = c(color_markers[c("IBL", "ORS", "HFSC")], bg_color),
                              breaks = c("IBL", "ORS", "HFSC", NA), na.value = bg_color) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() + Seurat::NoLegend()
```


Project name :

```{r fig_traj_project_name, fig.width = 4, fig.height = 4}
# Random order
set.seed(1234)
rnd_order = sample(colnames(sobj_traj), replace = FALSE, size = ncol(sobj_traj))

# Extract coordinates
cells_coord = sobj_traj@reductions[[name2D]]@cell.embeddings %>%
  as.data.frame() %>%
  `colnames<-`(c("Dim1", "Dim2"))
cells_coord$project_name = sobj_traj$project_name
cells_coord = cells_coord[(rnd_order), ]

# Plot
ggplot2::ggplot(cells_coord, aes(x = Dim1, y = Dim2, col = project_name)) +
  ggplot2::geom_point(size = 1.2) +
  ggplot2::scale_color_manual(values = sample_info$color,
                              breaks = sample_info$project_name) +
  ggplot2::theme_void() +
  ggplot2::theme(aspect.ratio = 1,
                 legend.position = "none")
```

Cluster :

```{r fig_traj_clusters, fig.width = 4, fig.height = 4}
Seurat::DimPlot(sobj_traj, reduction = name2D, pt.size = 1,
                group.by = "seurat_clusters", label = TRUE) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()
```

Cluster type :

```{r fig_traj_cluster_type, fig.width = 2, fig.height = 2}
Seurat::DimPlot(sobj_traj, reduction = name2D, pt.size = 1,
                group.by = "cluster_type", cols = color_markers) +
  ggplot2::theme(aspect.ratio = 1) +
  Seurat::NoAxes() +
  Seurat::NoLegend()
```

Pseudotime :

```{r fig_traj_pseudotime, fig.width = 6, fig.height = 4}
Seurat::FeaturePlot(sobj_traj, reduction = name2D, pt.size = 0.5,
                    features = "pseudotime") +
  ggplot2::scale_color_gradientn(colors = viridis::viridis(n = 100)) +
  ggplot2::lims(x = range(sobj_traj@reductions[[name2D]]@cell.embeddings[, 1]),
                y = range(sobj_traj@reductions[[name2D]]@cell.embeddings[, 2])) +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_blank()) +
  Seurat::NoAxes()
```

Pseudotime with dynplot's function :

```{r fig_traj_pseudotime_dynplot, fig.width = 6, fig.height = 6}
dynplot::plot_dimred(trajectory = my_traj,
                     dimred = sobj_traj[[name2D]]@cell.embeddings,
                     # Cells
                     color_cells = 'pseudotime',
                     size_cells = 1.6,
                     border_radius_percentage = 0,
                     # Trajectory
                     plot_trajectory = TRUE,
                     color_trajectory = "none",
                     label_milestones = FALSE,
                     size_milestones = 0,
                     size_transitions = 1)
```

# R Session

```{r sessioninfo, echo = FALSE, fold_output = TRUE}
sessionInfo()
```

